Running head: THE BRAIN DISEASE MODEL OF ......THE BRAIN DISEASE MODEL OF ADDICTION 4 addiction were present, four of which are the same models as Mosher and Akins (2007), with the

Running head: THE BRAIN DISEASE MODEL OF ADDICTION

The Brain Disease Model of Addiction and Implications for Public Stigma: A Cross-National

Study

by

Samantha Marie Rundle

A thesis submitted in conformity with the requirements for the degree of Master of Arts

Graduate Department of Psychology University of Toronto

© Copyright by Samantha Marie Rundle 2019

THE BRAIN DISEASE MODEL OF ADDICTION ii

The Brain Disease Model of Addiction’s Implication on Public Stigma: A Cross-National Study

Samantha Marie Rundle Master of Arts

Graduate Department of Psychology University of Toronto

2019

Abstract

The definition of addiction impacts stigma levels attributed to the addictive population.

Researchers in the United States (US) believe a brain disease model of addiction (BDMA)

reduces stigma, though other researchers worldwide disagree. Via MTurk, data was collected

from Canada, the US and Australia. Participants were randomized to one of four vignette

manipulations describing an individual with the following condition(s): addiction, mental health,

co-occurring addiction and mental health, or non-psychiatric medical. Participants’ beliefs in five

models of addiction and stigma attributed to the individual was measured. Addiction was the

most stigmatized condition though greater beliefs in the nature and psychological MOA

predicted lower stigma. Beliefs in the psychological, nature, and sociological MOA moderated

the vignette condition and stigma relationship and the moral MOA mediated the geographical

region and public stigma relationship. US accepted the BDMA more than Canada, although

greater beliefs in the BDMA did not predict lower public stigma.

ii

THE BRAIN DISEASE MODEL OF ADDICTION iii

Table of Contents

Abstract.........................................................................................................................................................................iiThe Brain Disease Model of Addiction’s Implication on Public Stigma: A Cross-National Study1

Models of Addiction..............................................................................................................................................................3BDMA.....................................................................................................................................................................................................4Moral Model of Addiction................................................................................................................................................................4Nature Model of Addiction...............................................................................................................................................................5Psychological Model of Addiction.................................................................................................................................................5Sociological/Compensatory Model of Addiction......................................................................................................................5Spiritual/Enlightenment Model of Addiction.............................................................................................................................6Disorder-of-Choice Model of Addiction......................................................................................................................................7Learning Model of Addiction..........................................................................................................................................................8Biopsychosocial Models of Addiction..........................................................................................................................................9

The Brain Disease Model of Addiction: Evidence...................................................................................................10Etiology and Progression.................................................................................................................................................................10Treatment and Recovery..................................................................................................................................................................11Ethical, Legal, and Social Implications of the BDMA..........................................................................................................12

Theoretical evidence for and against the BDMA.....................................................................................................15Clinical Implications of BDMA Beliefs: Empirical Evidence.............................................................................17Acceptance and Endorsement of the BDMA: Empirical Evidence....................................................................20The Current Study...............................................................................................................................................................23

Hypotheses...........................................................................................................................................................................................23Method.........................................................................................................................................................................24

Participants............................................................................................................................................................................24Materials.................................................................................................................................................................................25

Demographic Form............................................................................................................................................................................25Vignette Manipulations....................................................................................................................................................................25Personal and Perceived Public Stigma Measures (PPPSM)................................................................................................26Public Attitudes about Addiction Survey (PAAS)..................................................................................................................27

Procedure................................................................................................................................................................................28Analysis Plan........................................................................................................................................................................28

Results..........................................................................................................................................................................30

Discussion...................................................................................................................................................................35Objective one........................................................................................................................................................................36Objective two........................................................................................................................................................................40Limitations and Future Research....................................................................................................................................41

References..................................................................................................................................................................45

THE BRAIN DISEASE MODEL OF ADDICTION 1

The Brain Disease Model of Addiction’s Implication on Public Stigma: A Cross-National Study

There has been a long, heated debate regarding the way addiction should be defined; is it

a disease, a moral failing, or maybe a combination of the two? In the pre-industrial era, we held

individuals with addiction morally responsible for their behaviour; we incarcerated them,

isolated them, and deemed them unsafe to the public at large (Barnett, Hall, Fry, Dilkes-Frayne

& Carter, 2018). After prohibition and World War II, a researcher by the name of Elvin Jellinek

stated that “alcoholism comes in people, not in bottles,” thereby indicating the individual

drinking the alcohol that was the issue (Kelly, 2018).

Nearing the end of the twentieth century, Leshner (1997) published an article titled

“Addiction Is a Brain Disease, and It Matters.” At the time, Leshner was the Director of the

National Institute on Drug Abuse (NIDA), which financially supports the majority of the world’s

research on the health aspects of drug abuse and addiction. In this article, Leshner argued that

addiction is a disease due to the fact that there had been major advances in the fields of

neuroscience and behavioural science understanding addiction and drug abuse (Leshner, 1997).

Further, he added that addiction is a brain disease because drug use, no matter the substance or

pattern of use, modifies brain function, with a more severe and persistent pattern causing more

significant damage to brain functionality.

In his article, Leshner also argued that addiction is not only a brain disease due to the

disease’s complexity, but concluded that addiction is a chronic, relapsing disorder as addiction is

hardly ever an acute illness but an illness with many lapses of substance use (Leshner, 1997).

Besides the biologically based evidence, Leshner stated that the public holds negative and

incorrect views toward individuals suffering from addiction. For instance, he stated that the

public believes individuals suffering from addiction are “bad people” who are unwilling to lead


moral lives and in order to make any progress in controlling drug abuse, the public must accept a

“chronic illness sufferer” view for those with addiction (Leshner, 1997).

Although it has been stated that the brain disease model of addiction (BDMA) is

currently the most prevalent model of addiction in the western world and drives discourse in both

the professional and public setting, no empirical research has been conducted to determine if the

BDMA is accepted in North America, nor whether there is a difference, cross-nationally, in the

level of acceptance of the BDMA (Lewis, 2018). Moreover, it is believed that the support for the

BDMA will only get stronger as NIDA, which funds the majority of the research on addiction,

prioritizes research with neurobiological styles of thinking about drugs (Vrecko, 2010).

While it seems like the western world continues to promote the BDMA, researchers from

other geographical regions continue to question whether addiction is best defined as a brain

disease since most researchers do not believe that this model captures addiction’s complexity

(Heather, 2017; Satel & Lilienfeld, 2017; Meurk et al., 2016a). This discrepancy between the

western world and other regions has led to a recent escalation in the debate regarding addiction’s

definition (Vrecko, 2010). For instance, Nick Heather founded an international network of

researchers and academics coined the “Addiction Theory Network” whose main goal is to

criticize and oppose the BDMA (Heather et al., 2018). That said, many addiction experts, from a

variety of disciplines, are offering their opinions on the operational definition of addiction as

well as theoretically discussing and experimentally analyzing the implications of terming

addiction a “brain disease.”

In order to highlight the importance of the current study to the field of addiction, I will

review and define the current models of addiction and describe the evidence supporting the

BDMA in regard to: (a) the etiology and progression of addiction; (b) the treatment and recovery


of addiction, and; (c) the ethical, legal and social implications of terming addiction a brain

disease. Additionally, the evidence supporting the BDMA will also be compared to

corresponding evidence supporting competing models of addiction. Subsequently, I will discuss

the theoretical evidence supporting and negating the BDMA and report on the empirical

evidence investigating the clinical implications of terming addiction a brain disease. Lastly, I

will also describe the empirical research investigating a range of individuals’ acceptance and

endorsement of the BDMA.

Models of Addiction

The two oldest models of addiction are the moral model and the disease model of

addiction. Over time researchers in the field of addiction did not agree on the etiology,

progression, or treatment of addiction and, as a result, many different definitions of addiction

emerged. For instance, Brickman et al. (1982) created a table which categorizes four models of

helping and coping and uses substance use (specifically alcohol use) as a way to exemplify these

models. Consequently, four definitions of addiction were developed; the disease model (i.e. the

BDMA), the moral model, the spiritual/enlightenment model, and the compensatory model of

addiction. The definitions of addiction are categorized in regard to the individual’s locus of

responsibility for developing their addiction, and locus of responsibility for treating their

addiction.

Mosher and Akins (2007) have since classified four models of addiction which are

categorized by societies’ attitudes toward drug use: nature model, disease/BDMA model,

psychological model and sociological model. Similarly, Broadus and Evans (2015) pooled all

past questionnaires measuring beliefs in addiction and after factor analysis, five models of


addiction were present, four of which are the same models as Mosher and Akins (2007), with the

fifth being the moral model of addiction (Broadus and Evans, 2015).

In addition to these five models of addiction, researchers have developed their own

independent models of addiction including the learning, disorder-of-choice, and biopsychosocial

models of addiction. The nine models of addiction are discussed in further detail below.

BDMA. In the past, the disease model suggested that the individual suffering from

addiction has some biological disposition which influenced the development of their addictive

disorder (Miller & Giannini, 1990). More recently, with major advances in the field of

neurobiology, researchers have argued that the disease of addiction is caused by an abnormality

in brain functionality deeply rooted in biology (Leshner, 1997). These brain abnormalities are

thought to be caused by repeated exposure to substances of abuse, and/or the behaviour of abuse,

as well as possibly by factors rooted in the genome (Volkow, Koob, & McLellan, 2016).

Researchers have concluded that addiction is not only a disease but, more specifically, a disease

that manifests in the brain (Leshner, 1997). Since 1997, after Leshner specifically stated that

addiction is a brain disease, the disease model of addiction developed into the BDMA and,

consequently, research in the field focused on the brain. In the literature, the BDMA and the

disease model of addiction are now synonymous.

Moral Model of Addiction. The moral model of addiction suggests that drug use is a

maladaptive personal choice which can be attributed to the individual having a moral failing

(Moyers & Miller, 1993). This means that the individual using drugs is fully responsible for their

continued drug use resulting in addiction. The moral model of addiction was the prominent

model of addiction during the pre-industrial era as society deemed individuals suffering from

addiction morally responsible for their behaviour (Miller, 1987). That being said, a few


researchers still believe in the moral model of addiction. Schaler (2011) states that individuals

with addiction are not forced to use the drug; they choose to use the drug. Further, he suggests

that addiction is not limited to drugs and uses the example that Einstein was addicted to physics

(Schaler, 2011).

Nature Model of Addiction. The nature model of addiction states that the curiosity and

interest in drugs of abuse is a natural predisposition to the human race. This natural

predisposition is a drive of innate motivation for humans to want to alter their state of

consciousness (Mosher & Akins, 2007). The nature model termed by Mosher and Akins (2007)

suggests that addiction is a dysfunction and an abnormal response to this innate drive to alter an

individual’s consciousness.

Psychological Model of Addiction. The psychological model of addiction focuses on

abnormalities within the individual suffering from the addictive disorder. For instance, the

psychological model of addiction suggests that individuals suffering from addiction embody

dysfunctional coping mechanisms and specific personality types which make them vulnerable to

the initiation of drug use and the persistent continued use of the drug (Monti, Kadden,

Rohsenow, Cooney & Abrams, 2002). In order for an individual to prevent themselves from

using drugs during recovery, the individual must develop and learn positive coping strategies

(Broadus & Evans, 2015).

Sociological/Compensatory Model of Addiction. In comparison to the individual-

perspective of addiction in the psychological model, the sociological model of addiction views

addiction from a social level. The sociological model of addiction suggests that the individual’s

environment, culture, and education act as the major factor in the development of addiction

(Mosher & Akins, 2007). For instance, Levy (2013), proposes a social model of addiction where


the individual’s social context is the driving factor which leads and perpetuates the individual’s

addiction and in order to recover from their addiction, they must change their social system and

environment. Levy (2013) provides an example of an individual with a peanut allergy; while the

peanut allergy is genetic, similar to addiction, it is still possible to alter the individual’s

environment in order to reduce or completely abolish any impairment from their peanut allergy.

The compensatory model of addiction, proposed by Brickman et al. (1982), also falls

under the sociological model of addiction since the individual is seen as deprived and/or

suffering from deficiencies in their environment and substances of abuse are a coping

mechanism. For instance, a homeless youth may turn to substance use as a way to cope with the

fact that they are homeless. Consequently, the individual in the compensatory model of addiction

is viewed as not responsible for the development of their disorder but are fully responsible for

the recovery as they must take action to change their environment in order to recover from their

addiction.

Similarly, Bruce Alexander introduced his famous study called “rat park” which gave rats

a new environment which was occupied by other rats (making it a social environment). This new

environment replaced the isolated cage that rats were previously housed in which only offered

them drug laced water and regular water. Alexander’s research suggested that when rats live in a

social environment with other things to do besides drinking the drug-laced water, they were less

likely to self-administer the drug-laced water than rats who lived in isolation (Gage & Sumnall,

2019). Consequently, this finding supports a sociological model of addiction where the etiology

and progression of addiction is driven by the rat’s social environment.

Spiritual/Enlightenment Model of Addiction. In 1935, two men, a stockbroker and a

surgeon from Akron, Ohio, were suffering from alcohol addiction. These two men found that


supporting one another to remain abstinent from alcohol helped them become sober and stay

sober in their recovery. Others within their city and in nearby cities started to join their support

group and started having weekly meetings. This in turn became the group called “Alcoholics

Anonymous” (AA; Nace, 2015). AA best reflects the spiritual model of addiction because within

the 12-Step program, individuals are asked to submit control to a power that is greater than

themselves, such as God. Today, AA is still a widely used resource for those suffering from

alcohol addiction. Additionally, more groups have been formed following the same guidelines

set forth by AA for other substance/behavioural use disorders (e.g. Narcotics Anonymous,

Gamblers Anonymous, etc.).

Disorder-of-Choice Model of Addiction. Researchers who promote a disorder-of-choice

model of addiction state that addiction is disease-like in the sense that it endures even when the

negative characteristics outweigh the positive characteristics. Yet, past research has also

indicated that a large majority of individuals with addiction who do quit and become abstinent,

do so without any pharmacological or medical assistance (Klingemann et al., 2010). With that,

researchers who promote a disorder-of-choice model of addiction state that voluntary behaviour

must be involved in addiction since there is evidence that some individuals with addiction are

able to just stop using substances.

One promoter of the disorder-of-choice model of addiction, Heather (2017), believes that

addiction results from the repetition of both voluntary and involuntary behaviour. Although

Heather (2017) does not fully believe that addiction is merely a choice, he believes that choice is

actively involved in addiction and must be involved in order for an individual to stop consuming

drugs. He believes that an individual with addiction may choose to stop taking their drug of

choice (voluntary behaviour) at time 1 and then at time 2, when faced with the decision to use


the drug or not, they succumb to their better judgment through a weakness of will (involuntary

behaviour) and use the drug rather than abstain from it (Heather, 2017). This example displays

an individual who has made a firm decision to cease drug use yet cannot stay true to their

decision when they are directly faced with the situation. Heather thus believes that individuals

suffering from addiction, suffer from a weakness of will, or “akrasia,” a term dated as far back as

Greek classical philosophers, which is defined as a lack of self-control or the state of acting

against one’s own better judgment.

Similarly, Heyman (2013) also promotes a disorder-of-choice model of addiction by

indicating that there is a level of choice in drug use that although may be irrational, is still a

choice; not compulsive behavior. Further, Heyman (2013) suggests that other diseases which

people recover from, such as cancer and heart disease have differing correlates for recovery than

the reasons put forth by individuals who have recovered from addiction. Such reasons include,

being a parent, getting married, and starting a career (Hayman, 2013).

Learning Model of Addiction. The learning model of addiction posits that addiction has

become habitual to an individual. For instance, Marc Lewis (2018) believes that drug use and

addiction are learned behaviours and consequently, drug use and addiction can also be unlearned.

Lewis’ theory proposes that the changes seen in the brain from drugs of abuse is normal

neuroplasticity (Lewis, 2018). More specifically, central arguments of the BDMA state that

addiction reduces the functional and structural connectivity between the striatum and prefrontal

cortex since underused synapses start to prune. Lewis competes with this notion by suggesting

that a streamlining process overrides underused synapses which eventually become pruned.

Lewis compares this process seen in addiction to individuals who play a professional sport since

their skills become habits. For example, a beginner football player may need to actively think


about the way in which a football is caught while catching the ball, but a professional no longer

thinks about the behaviour; it becomes muscle memory.

Lewis believes that this process that BDMA promoters state are impacting the functional

and structural connectivity between the striatum and prefrontal cortex is just a key mechanism of

learning, and if the individual remains abstinent for a long period of time, Lewis believes brain

structure will return to normal levels of functionality; addiction is learned and can be unlearned

(Lewis, 2017).

Biopsychosocial Models of Addiction. The biopsychosocial model of addiction suggests

that addiction is caused by a combination of genetic predispositions as well as psychological,

social, and cultural aspects of the individual’s life. For instance, Satel & Lilienfeld (2017)

propose a multi-dimensional definition of addiction where an individual’s genetics, psyche,

social environment, and culture all influence the development of addiction. They argue that these

four dimensions are not hierarchically structured but instead are placed side-by-side in a

horizontal plane to display how each dimension is complex in its own way and how each

dimension can interact with one another equally.

Similarly, Snoek (2017) proposes a stage-like model of addiction where individuals can

enter a disease stage which is defined as an individual losing complete control over their life.

This disease stage is surrounded by other stages where individuals may have partial or complete

control over their life. Individuals can enter the stage-like model of addiction through a vast

variety of causes including a genetic predisposition, a sociological issue, or a developmental

issue. Overall, Snoek (2017) suggests that addiction is not a disease but a condition that may be

present for a certain period of time and may recur multiple times (i.e. lapses during recovery).


The Brain Disease Model of Addiction: Evidence

Etiology and Progression. Proponents of the BDMA suggest that within the last decade,

there have been major advances in neurobiology, which clarifies the mechanisms underlying

addiction (Volkow, Koob, & McLellan, 2016). For instance, a main proponent of the BDMA is

Nora Volkow, the current director of NIDA. Volkow and her colleagues (2016) propose that at

the neurobiological level, dependency is established because each drug use episode increases the

release of dopamine, which elicits a reward signal that in turn triggers Pavlovian learning and

conditioning. With repeated exposure to the same reward from the drug, dopamine cells stop

firing with drug use but instead fire to the anticipation of the drug’s reward. Therefore,

contextual and environmental cues become conditioned stimuli and trigger the release of

dopamine in the drug user (Schultz, 2002). These conditioned responses are then mediated by the

amygdala, hippocampus, and ventral prefrontal cortex, which are involved in emotional

reactivity, memory, and salience attribution, respectively (Volkow & Morales, 2015). The

dopaminergic increase felt by the drug user upon exposure to these contextual and environmental

cues are believed to promote drug use. Drug use is then continuously repeated because exposure

to the drug increases dopaminergic activity which is believed to trigger the desire to use the drug;

ultimately sustaining and perpetuating the motivation to use drugs (Volkow & Morales, 2015).

Comparison with other models of addiction. Many models of addiction differ from the

BDMA in regard to the etiology and progression of addiction. For instance, the moral model of

addiction suggests that addiction is a lifestyle choice where drug use is both initiated and

compulsively continued by choice rather than by a biological process (Miller, 1987).

Additionally, the sociological/compensatory model of addiction proposes that addiction’s

etiology stems from a deficiency of goods and resources that each individual is entitled to, such


as family, love, and shelter and the progression of drug use is defined as using substances of

abuse to compensate for the lack of these resources (Brickman et al., 1982).

Treatment and Recovery. Alongside empirical evidence supporting the BDMA in

regard to the etiology and the progression of addiction, there is also empirical evidence

supporting the BDMA for the treatment and recovery of addiction. Volkow and her team (2016)

suggest that the evidence supporting medical treatment for addiction is strong. For instance, they

believe that medication helps restore healthy functioning in the dysfunctional brain circuitry

which leads to improvements in behaviour, such as ceasing drug use by staying abstinent and

restoring normal emotional and decision-making capabilities (Volkow, Koob, & McLellan,

2016). Some specific empirically supported medications for substance use disorders are

Bupropion for nicotine dependence, Naltrexone for alcohol dependence, and Methadone for

opioid dependence (Stapleton et al., 2013; Aubin & Daeppen, 2013; Donny, Walsh, Bigelow,

Eissenberg, & Stitzer, 2002). BDMA proponents have interpreted the result of these medications

being efficacious for treating dependence as evidence for the validity of the BDMA.

Comparison with other models of addiction. In clinical practice, medications are often

used in conjunction with psychosocial therapies such as cognitive-behavioural therapy.

Cognitive-behavioural therapy is an exemplary treatment strategy for the psychological model of

addiction as the clinician may believe that the addict’s thought process and subsequent behaviour

is essential to a successful recovery. Specifically, coping skills must be learned and maladaptive

thought patterns must be challenged in order to recover from their addiction (Monti et al., 2002).

Although pharmacological medication may be used in combination with cognitive-behavioural

therapy, differences exist between the two modalities of treatment. Supporters of the BDMA

suggest medication is the most important treatment for addiction whereas the psychological


model of addiction supporters believe that coping skills and psychosocial approaches to

treatment are most important (Volkow, Koob, & McLellan, 2016; Kloss & Lisman, 2003).

The moral model of addiction, the spiritual model of addiction, and Heyman’s (2013)

disorder-of-choice model of addiction compete with the BDMA in regard to treatment and

recovery as they all rely heavily on choice; that is, the individual must choose to stop using the

drug. In the moral model of addiction, one must choose to stop using the drug and continue to

“say no” to the drug whenever the individual is confronted with it (Brickman et al., 1982). It is

also believed that the individual suffering from addiction fails to solve their problem because

they are unmotivated to do so; they want to continue using the drug. In the spiritual model of

addiction, the individual suffering from addiction must be enlightened to the root cause of their

problem and accept a strong degree of submission to agents greater than themselves; the first

step in the 12-step program in AA (Nace, 2015). Subsequently, individuals in AA must embody

a higher level of motivation to maintain their sobriety and rely on others in the program to guide

them when they are craving their old behaviour.

Ethical, Legal, and Social Implications of the BDMA. Many implications will arise if

addiction is accepted by society at large as a “brain disease.” For instance, researchers indicate

that many individuals who suffer from addiction do not believe that they have a brain disease

(Lewis, 2017). Thus, if the individuals suffering from addiction do not believe they have a

disease, is it ethical to label them as having one?

Implications may also arise in the legal system if the BDMA is accepted. By terming

addiction a brain disease, the judicial system and its authorities may rely less on imprisonment

for substance abusers, as the individual in question might be seen as having less responsibility

over their actions. Instead, the judicial system may have a more sympathetic attitude toward


individuals with addiction and see them as people who need support and treatment. Thus,

individuals with addiction may be pardoned from criminal activity by pleading insanity from

their addiction (i.e. they were not in control of their behaviour due to their brain disease).

There have also been notable changes in medical insurance companies in the United

States by including addiction treatment services in healthcare plans. More specifically, addiction

treatment services have been included in the most basic health plans as addiction treatment is

being viewed as a disease where the individual needs access to medication and treatment

(Volkow & Koob, 2015).

Differing views also exist on the consequences that may arise in regard to research

funding if “brain disease” terminology is accepted or not (Volkow, Koob, & McLellan, 2016,

Volkow & Koob, 2015; Leshner, 1997; Hall & Carter, 2013; Buchman, Illes, & Reiner, 2011;

Berridge, 2017). For instance, Hall and Carter (2013) believe that by terming addiction a “brain

disease” it will increase funding for addiction research and therefore lead to more effective,

biologically based treatments for addiction. Similarly, Berridge (2017) believes that if the

BDMA is rejected, the public will not have sympathy for individuals suffering from addiction

and, consequently, individuals in the public will be more likely to blame those with addiction for

their addiction. Consequently, treatment will change and research will become obsolete as there

would be no reason to fund addiction research if addiction is just a choice (Berridge, 2017).

Lastly, and perhaps most importantly with respect to the present work, by terming

addiction a “brain disease” it is hoped that individuals in society will view individuals with

addiction as not morally responsible for their addiction, which in turn will decrease public stigma

toward the diseased population (Volkow, Koob, & McLellan, 2016). “Brain disease”

terminology has been introduced with the intent that as public stigma reduces, treatment seeking


for the diseased population increases, consequently leading to fewer individuals suffering from

the disease and more people accessing treatment for it. Although the intent is good, there is no

empirical evidence demonstrating that applying “brain disease” terminology has in fact had its

intended effect; that is, to reduce public stigma associated with addiction (Heather, 2017).

Further, there is no empirical evidence that a reduction in public stigma will lead to an increase

in treatment seeking.

That said, in the field of mental health disorders, it has been shown that there is less

blame attributed to individuals with a mental illness if there is a biological explanation for the

disease (Kvaale, Haslam, & Gottdiener, 2013; Lebowitz, Pyun, & Ahn, 2014). Since mental

health disorders and addictive disorders are similar in nature (as they are not visible disorders to

the public), it may be true that by associating a biological explanation for addictive disorders,

public stigma will also be reduced. On the other hand, Room, Rehm, Trotter, Paglia, and Ustun

(2001) have suggested that mental health disorders are less stigmatized than addictive disorders.

Thus, it is also possible that since addictive disorders are believed to be more stigmatized than

mental health disorders worldwide, “brain disease” terminology may have no effect on public

stigma (Room et al., 2001).

Comparison with other models of addiction. Within both the moral and

spiritual/enlightenment model of addiction, individuals are seen as responsible for the

development of their disorder. Thus, individuals suffering from addiction are seen as ethically,

legally, and medically responsible for their disorder (Nace, 2015; Moyers & Miller, 1993). In the

psychological model of addiction, where the individual must learn positive coping strategies,

individuals with addiction may not be seen as medically responsible for their disorder as they are

able to rely more on professionals, as well as themselves, for their recovery (Riper et al., 2014).


Theoretical evidence for and against the BDMA

As mentioned previously, Volkow, Koob, and McLellan (2016) strongly suggest that

addiction is most accurately defined as a brain disease due to the neurobiological evidence found

in neuroimaging studies. They suggest that neuroimaging studies show that addiction causes a

desensitization of reward circuits, increased craving, and weakening of the brain regions which

impact decision making, inhibitory control, and self-regulation (Volkow, Koob, & McLellan,

2016). Hall and Carter (2013) also agree with Volkow, Koob, and McLellan (2016) that there is

a genetic component in addiction. Hall and Carter (2013) suggest that twin and adoption studies,

as well as correlations between genetic markers, provide evidence for a BDMA. More

specifically, twin studies have indicated that genetic factors contribute substantially to the risk of

the development of alcohol, nicotine, and cannabis addiction with heritability ranging from 40-

60% (Hall, Carter, & Forlini, 2016).

Similarly, Berridge (2017) suggests that there are two forms of extreme parameter brain

changes that happen in addiction. The first is mesolimbic suppression which is caused due to a

down-regulation of dopamine receptors that especially occur in situations where an individual is

vulnerable to addiction (Berridge, 2017). The second is mesolimbic hyper-activity which is

induced by a history of drug binges that elicit excessive “wanting” or incentive salience

(Berridge, 2017).

Conversely, other researchers in the world disagree. Lewis (2017) suggests that the

BDMA is flawed because the brain changes seen in the neuroimaging studies cited by Volkow,

Koob, and McLellan (2016) are similar to those generally observed in the development of habits

and Pavlovian learning. More specifically, he believes that delay discounting, motivational


amplification, and the rapid dissipation of the effect of drugs are the three mechanisms

underlying addiction (Lewis, 2018).

Similarly, Heather (2017) also supports a non-BDMA definition of addiction. Heather

(2017) states that one of the only reasons why the BDMA is so widely promoted is that it is

hoped that the definition will reduce stigma. Heather (2017, 2018) does not believe that defining

addiction as a “brain disease” will decrease public stigma nor does he think that the public has

accepted “brain disease” terminology. Further, Heather (2017) also suggests that the claim

stating the BDMA is the only way to ensure access to treatment is distorted; he believes that the

public will be able to understand and accept a definition of addiction that is neither a brain

disease nor a moral failing but rather, a model that includes voluntary drug seeking and drug

taking yet is still a behaviour that is extremely hard to change (Heather 2017).

Closely related to Heather (2017), Schaler (2011) believes in a moral model of addiction.

He argues that addiction is an old English word meaning devotion, commitment, and attachment.

Further he suggests that all individuals with addiction choose their addictions and everyone in

society may have an addiction at some point in their lives; such as an addiction to their spouse or

their work (Schaler, 2011). Consequently, Schaler (2011) has suggested that all past evidence

conducted through empirical studies support the view that individuals with addiction are

conscious, responsible people, who are in full command of their behaviour, as they can, in

actuality, refrain from injecting themselves with heroin and can also refrain from swallowing

alcohol (Schaler, 2011).

Other researchers from different backgrounds also provide a multidisciplinary view to the

definition of addiction. For example, researchers who support a stage-like model of addiction

believe that only those individuals who are the most severe can be labeled as having a “brain


disease” (Snoek, 2017; Fenton & Weirs, 2017; Hall & Carter, 2013; Snoek & Mathews, 2017).

Many researchers also believe that the BDMA does not capture the complexity of addiction

(Satel & Lilienfeld, 2017; Meurk et al., 2016a). For instance, Satel and Lilienfeld (2017) view

addiction as a complex set of behaviours that operate on several dimensions. These dimensions

range from molecular functioning and structuring, the psychological environment, the social

relations of the individual, and brain physiology (Satel & Lilienfeld, 2017). Further, they suggest

that individuals who have brain alterations due to drug use are sometimes unable to make

decisions and find it more difficult to carry out their decisions while confronted with them.

Though this does not eliminate their capacity to choose whether to use the drug in front of them

or not (Satel & Lilienfeld, 2017).

Clinical Implications of BDMA Beliefs: Empirical Evidence

Surprisingly, there is a lack of empirical evidence within the field of research

investigating the implications of the BDMA in society. Supporters of the BDMA claim that

society holds a stigma toward individuals suffering from addiction which is comprised of

blaming these individuals for having a moral failing (Leshner, 1997; Volkow, Koob & McLellan,

2016). These supporters believe that the only way to combat this stigma is to promote a brain

disease model of addiction as it will foster a more sympathetic view of individuals with

addiction; they don’t have a moral failing, they have a brain disease.

On the other hand, researchers who are against the BDMA are hesitant to term addiction

a “brain disease” as they believe there may be some negative consequences from this label

(Lewis, 2017; Heather, 2017; Satel & Lilienfeld, 2017; Hall & Carter, 2013; Hall, Carter &

Barnett, 2017; Levy, 2013; Snoek, 2017). Such negative consequences are that “brain disease”

terminology will increase stigma and promote a greater societal distance toward individuals with


addiction (Lewis, 2017; Heather, 2017). Additionally, some researchers wonder what

implications these beliefs have on clinical-decision making, such as clinician’s treatment

selection for their patients and patients’ acceptance of their treatment program (Barnett & Fry,

2015). In other words, do treatment providers’ beliefs in a BDMA impact their treatment

selection process for their client, such that if a treatment provider believes in the BDMA they are

more likely to utilize medicinal treatments for that patient in comparison to a treatment provider

who does not support a BDMA? Similarly, if a client believes in a BDMA, are they more likely

to benefit from a treatment plan with a biological focus in comparison to other treatment plans

that may be offered?

Due to this debate, researchers in the field, mostly in Australia, have started to determine

the implications of terming addiction a brain disease. For instance, researchers have investigated

implications for clinician and patient beliefs of the BDMA. One study conducted in Australia

investigated whether clinicians supported the BDMA, whether these attitudes had an impact on

clinical treatment, and how they believed the BDMA would impact their clients’ behaviours

(Barnett & Fry, 2015). Face-to-face interviews were conducted with the participant’s where they

were asked about their previously established acceptance and awareness of the BDMA. Results

from this study suggested that clinicians did not accept the BDMA as they believed that

addiction is more multifactorial than the way the BDMA is defined; they believed that the

BDMA ignored the key social, psychological, and environmental factors which they deemed

important to the successful treatment of addiction. Moreover, these clinicians believed that there

are both positive and negative implications on client’s who accept the BDMA. Positive

implications included an increased insight into their problems as well as decreased stigma.


Negative implications, on the other hand, included increased stigma, a sense of helplessness, and

a reduction in personal responsibility to their addiction (Barnett & Fry, 2015).

Another study conducted in Australia investigated how clients in treatment for drug and

alcohol addiction believe they became addicted to their drug of choice and what role, if any,

neurobiological processes played in this development (Meurk et al., 2016a). Participants in the

sample commonly defined addiction as a cyclical behaviour, a loss of control, being dependent

on a drug, and a complex condition. It was less common for a participant to mention biological

explanations of dependence. Participants believed that they became addicted to their drug of

choice as a result of individual motivation, choice, and/or personal attributes. Participants

indicated that they wanted addiction to be treated as more of a health concern than a moral or

criminal issue, but when asked whether they wanted to be seen as “sick,” they were divided

(Meurk et al., 2016a).

Similarly, another Australian study investigating addiction compared neuroscientist’s and

clinician’s views on how the BDMA would impact addicted individuals’ beliefs and behaviours

(Bell et al., 2014). This sample of neuroscientists and clinicians believed that the BDMA could

help addicted individuals understand their own behaviour, which could possibly facilitate

change. Patients may feel less guilty about their addiction as they feel less personally responsible

for the development of it. Although the neuroscientists and clinicians saw the benefit in the

BDMA, they also voiced concern around whether the BDMA could hinder addicted individuals’

behaviour change, and reduce their willingness to enter treatment. Further, they believed that the

model may undermine their ability to reduce drug use and provide addicted individuals with an

excuse to not attempt behaviour change.


Acceptance and Endorsement of the BDMA: Empirical Evidence

The above Bell et al. (2014) study also examined the extent to which addiction

neuroscientist’s and clinician’s accepted/endorsed the BDMA. The clinicians in the sample were

less accepting and more skeptical of the BDMA than were the neuroscientists, but despite this

skepticism, all but one clinician stated that they utilized neuroscientific explanations in their

treatment regimen. In regard to the neuroscientist sample, there were slightly more individuals

who were against/skeptical of the BDMA than individuals who supported the BDMA. Overall,

less than one-third of the Australian neuroscientists and clinicians in this sample endorsed the

BDMA (Bell et al., 2014).

Researchers have also investigated implications for the public’s and family members

beliefs of the BDMA. For instance, Meurk et al. (2016b) conducted a study examining the

definition of addiction, how it is caused, and explanations of the BDMA amongst family

members of individuals with addiction. Results showed that participants believed that the reason

why their relative developed an addiction was due to the fact that it was their choice. In addition,

family members felt that poor self-esteem, personality, easy access, genetic makeup, risk-taking,

and brain chemistry were also contributing factors to the development of their addiction.

Addiction as a disease was endorsed by 47% of the family members with 33% specifically

endorsing a brain disease. Those who denied the “brain disease” terminology believed that the

label negated the responsibility of the individual’s behaviour. Additionally, they believed that it

may increase stigma toward addicted individuals and could possibly give the individual the

impression that it is difficult to quit. Overall, family members believed that there is no single

“best” way of understanding addiction though the majority felt the development of addiction was


a combination of personal choices, psychological factors, traumatic events, and

biological/genetic makeup.

Another study, also conducted in Australia, examined whether nicotine smokers, who

were receiving treatment for their addiction, accepted the BDMA (Morphett, Carter, Hall, &

Gartner, 2017). Results for this study suggest that a minority of smokers in this sample agreed

with the BDMA even though most agreed that nicotine acts on the brain to influence their

smoking. Participants indicated that they choose to smoke nicotine and that the “brain disease”

label is too serious to apply to cigarette smoking, suggesting that the “brain disease” label may

apply to some addictions but not nicotine addiction. One participant even echoed Marc Lewis by

stating “what you can learn, you can unlearn” (Morphett et al., 2017). The participants' major

concern with the BDMA was that such terminology would increase stigma and prejudice against

smokers. They also believed that this would lead smokers to absolve themselves of their personal

responsibility for smoking. Lastly, participants believed that most smokers would reject this label

even if it were scientifically accurate, as they do not associate their behaviour with a disease

(Morphett et al., 2017).

One last empirical study investigating “brain disease” terminology was examined

amongst the public in Queensland, Australia (Meurk et al., 2014). This study aimed to

investigate the public’s understanding of alcohol and heroin addiction. Participants were

presented with scenarios of two addicted persons, one suffering from an alcohol use disorder and

the other suffering from a heroin use disorder. Participants were then asked questions about

definitions and causes of addiction. Results suggested that over half of the participants accepted

the disease model of addiction with fewer accepting the BDMA. Those participants who agreed


that addiction has a biological cause were more likely to agree with the BDMA. The authors

suggest that future research is needed to determine the impact of these beliefs on stigma.

Most of the research in the field thus far has been in the form of theoretical papers and

personal views on whether the BDMA is the correct definition of addiction, without efficacious

empirical evidence supporting their arguments. The limited empirical evidence investigating the

acceptance and endorsement of BDMA beliefs seems to suggest that there is little acceptance and

endorsement of the BDMA. Additionally, although public stigma has been discussed thoroughly

throughout the theoretical papers as a major implication of BDMA beliefs, there is little

empirical evidence of how BDMA beliefs relate to public stigma (Heather, 2017).

Volkow, Koob, and McLellan (2016) suggest, with no empirical evidence, that the

introduction of “brain disease” terminology will decrease public stigma toward individuals

suffering from addiction. Although this result would be beneficial and conducive to treatment

seeking, as they suggest, others in the field feel as though “brain disease” terminology may

actually do the opposite and increase stigma by labelling individuals suffering from addiction

“sick” and “diseased” (Lewis, 2018; Heather, 2017).

Past research has indicated that attributing mental health disorders to a biological cause

lessened the public stigma to those suffering from them (Kvaale, Haslam & Gottdiener, 2013).

Supporters of the BDMA suggest that this trend will also be seen if addiction is triggered by a

biological cause. With this, it may be interesting to investigate whether the co-occurrence of a

mental health disorder with an addictive disorder (a co-morbid condition) produces lower stigma

ratings toward that individual relative to an individual with an addictive disorder alone.

Additionally, since the debate in the field contests whether “brain disease” terminology reduces

public stigma, it is important to empirically investigate whether greater acceptance/beliefs in the


BDMA do in fact impact public stigma attributed to individuals with addiction. Lastly, since

promoters of the BDMA are typically situated in the United States and non-supporters of the

BDMA are typically from other regions of the world, it is important to investigate whether the

public’s geographical region has an influence on their belief of the BDMA and/or public stigma

toward individuals suffering from addiction.

The Current Study

The current study examined whether acceptance of five models of addiction (BDMA,

moral, nature, sociological and psychological) predicted perceived public stigma to hypothetical

individuals in one of four disorder conditions as described in the corresponding vignettes: a) an

individual suffering from addiction; b) an individual suffering from a mental health disorder; c)

an individual suffering from a co-occurring mental health and addictive disorder; d) and an

individual suffering from a non-psychiatric medical disorder. More specifically, the study

investigated whether the vignette condition predicted public stigma toward the respective

individual in the vignette and, further, whether that relationship was moderated by the

participants’ acceptance and beliefs in the five models of addiction measured. Lastly, the current

study examined whether participants’ geographical region predicted the level of public stigma

they attributed to the individuals in the vignette conditions, and whether this relationship was

mediated by the participants’ endorsement and acceptance of the five models of addiction

measured.

Hypotheses. Based on past research suggesting that mental health disorders are less

stigmatized than addictive disorders, it is hypothesized that, overall, public stigma ratings toward

the individual in the mental health condition will be lower than the individual in the addiction

condition. Moreover, we will investigate whether there is a difference between public stigma


ratings toward the individual in the addiction condition and the individual in the co-occurring

mental health and addiction condition. If less stigma is attributed toward the individual in the co-

occurring mental health and addiction condition than the addiction condition alone, it may

suggest that public stigma is reduced with the presence of a mental health disorder.

Secondly, since proponents of the BDMA suggest that greater acceptance of the BDMA

will decrease public stigma in our society, it is hypothesized that there will be a significant

interaction between experimental vignette condition and beliefs in the BDMA. It is hypothesized

that an interaction will exist where as beliefs in the BDMA increase, predicted public stigma

ratings will be low for participants who view the addiction conditions (addiction condition and

co-occurring addiction and mental health condition) and public stigma ratings for participants

who viewed the other two conditions (mental health and non-psychiatric medical condition) will

not change.

Lastly, since “brain disease” terminology is used more in the United States due to the

proponents of the BDMA and the NIDA promoting the terminology, it is hypothesized that there

will be greater acceptance of the BDMA in the United States in comparison to Canada and

Australia.

Method

Participants

Data were collected from a total of 1072 participants (Male = 61.5%, Caucasian = 67.2%)

through Amazons Mechanical Turk (MTurk) from the three geographical regions: Canada (N =

289), the United States (N = 713), and Australia (N = 70). Participants who failed the vignette

manipulation (i.e. incorrectly identifying the disorder of the individual in their study condition

after reading the vignette) were excluded from analysis leaving a total of 872 participants (Male


= 60.2%, Caucasian = 69.5%) from Canada (N = 248), the United States (N = 574), and

Australia (N = 50). The United States was chosen as a region to be measured as many of the

major proponents of the BDMA reside in the United States and NIDA funds studies with a

neurobiological focus (Heather, 2017). Australia was chosen as a region to be measured since the

empirical research analyzing beliefs in the BDMA has all taken place in Australia. Within each

geographical region, participants were randomly assigned to one of the four vignette conditions;

addiction condition (N = 230), mental health condition (N = 228), co-occurring addiction and

mental health condition (N = 190), and non-psychiatric medical condition (N = 224).

Recruitment was limited to those who can read and write in English. All participants were

compensated $1.00 USD for their time.

Materials

Demographic Form. Participants completed a basic demographic form asking sex, age,

ethnicity, location of residence, level of education, employment status, and level of income (see

Appendix A).

Vignette Manipulations. Eight vignette conditions, adapted from Link, Phelan,

Bresnahan, Stueve, and Pescosolido (1999), were created by the researchers to investigate

whether there is a difference in the level of stigma attributed to the study conditions. The

vignettes depict a hypothetical person with one of the following conditions: (1) alcohol use

disorder; (2) major depressive disorder; (3) a co-occurring alcohol use and major depressive

disorder; (4) diabetes. For each of the four conditions listed, there was both a male and a female

version (John and Jane), therefore creating eight conditions in total. Vignettes were designed to

be as similar as possible to one another except for the variable which was being manipulated (see

Appendix B). After reading the vignette, participants were asked to answer a multiple-choice


question asking which of the disorders John/Jane had in the vignette. This question was designed

to identify those participants who read and understood the vignette and those who did not. As

stated above, participants who failed this vignette manipulation check were excluded from the

analysis.

Personal and Perceived Public Stigma Measures (PPPSM). The PPPSM is a 23-item

survey and was specifically chosen as a measure for the current study as it was created with the

intention to be completed after participants read a vignette condition (see Appendix C).

Additionally, the PPPSM is one of the only measures that quantifies stigma from a public

perspective in comparison to self-stigma which is stigma felt by the individual. Public stigma in

the current study is measured via the PPPSM’s four subscales; a) perceived public stigma; b)

perceived treatment stigma; c) personal stereotypical/prejudicial stigma; and d) personal

discriminatory stigma, with Cronbach’s α = .731, α = .737, α = .720, and α = .843, respectively.

The first, second, and some items in the third subscale are measured on a 4-point Likert scale

from 1 strongly disagree to 4 strongly agree. The remaining items on the third subscale are

measured on a 4-point Likert scale ranging from 1 not at all likely to 4 very likely. Lastly, the

fourth subscale is measured on a 4-point Likert scale ranging from 1 definitely unwilling to 4

definitely willing (Holman, 2015). An example of an item in the perceived public stigma subscale

is “People like them should feel embarrassed about their situation.” An example of an item in the

perceived treatment stigma subscale is “If people know they were in treatment, they would lose

friends.” Additionally, an example item from the personal stereotypical/prejudicial stigma

subscale is “People like them are unpredictable.” And lastly, an example item from the personal

discriminatory stigma subscale is “I would be willing to have them care for my children.” In


regard to analyses for the current study, scores were combined from all four subscales, creating

one public stigma score for each participant.

Public Attitudes about Addiction Survey (PAAS). The PAAS is a 54-item survey

which measures individual’s beliefs in and acceptance of specific models of addition (see

Appendix D). More specifically, the PAAS was chosen as a measure in the current study as it

was created with the intent to consolidate 8 previously established instruments which measured

beliefs in models of addiction. Therefore, the items in the PAAS reflect the most relevant items

of all past surveys measuring the beliefs in the models of addiction. Through factor analysis, the

PAAS identified 5 models of addiction: (1) disease (6 items); (2) moral (16 items); (3)

psychology (15 item); (4) sociology (7 items); and (5) nature (10 items) with Cronbach’s α =

.703, α = .894, α = .869, α = .804, and α = .832, respectively. Participants responses were based

on a 7-item Likert scale from 1 strongly disagree to 7 strongly agree. Each of these models of

addiction are defined in the first section of this thesis exploring the nine models of addiction. An

example item from the disease subscale is “Drug use changes the brain after a few exposures and

causes addiction.” An example of an item from the moral subscale is “Addiction is a choice.” An

example of an item from the psychology subscale is “Traumatic events may lead to addiction.”

An example of an item from the sociological subscale is “Addicts can learn to control their use.”

And lastly, an example of an item from the nature subscale is “As long as no one else is harmed,

people should have the right to engage in whatever behaviors they want.” In the current study,

the participant’s magnitude of belief in the 5 models of addiction was measured and

consequently each participant had 5 scores corresponding to their level of acceptance of each

model.


Procedure

Through MTurk, participants selected the current study by clicking on a link which directed

them to the external survey software, Qualtrics. After reading and agreeing to the consenting statement,

each participant filled out the demographic questionnaire (see Appendix A). After the completion of the

demographic questionnaire participants read one of the eight brief (5-sentence) vignettes. Participants

allocation to the vignette was determined at random using a feature provided in Qualtrics (see Appendix

B for vignette conditions). Subsequently, participants completed the PPPSM which determined the

amount of stigma they attributed toward the individual in the vignette condition. Next, each participant

filled out the PAAS. There was no time limit for individuals to complete the study. Upon completion of

the Qualtrics survey, participants were given a randomized 5-digit code and were asked to copy and

paste the code into MTurk in order to indicate they completed the study. This randomized code was then

checked with our records to determine that the participant completed the survey and did not duplicate

another participant’s code before compensation was distributed.

Analysis Plan

Data will be checked for validity/random responding and completeness. Upon collection,

the data will be cleaned, reverse scores will be corrected, and outliers above and below 3 SD

from the mean will be windsorized. First, SPSS will be used to conduct a univariate between-

subjects ANOVA to determine whether there were differences in mean public stigma ratings

depending on what vignette condition the participant was randomized to. In this ANOVA, public

stigma ratings will act as the dependent variable and vignette condition will act as the

independent variable. If a significant result is found, Tukey’s HSD post-hoc analysis will be

conducted to determine which vignette conditions significantly differ from each other on mean

public stigma ratings.


Additionally, to answer the second hypothesis, interaction terms representing the

interactions of each model of addiction with vignette condition will be inputted into a moderation

model to examine whether beliefs in any of the five models of addiction, measured through the

PAAS, moderate the relationship between all four vignette conditions and public stigma,

measured through the PPPSM. In other words, public stigma will act as the dependent variable,

vignette condition will act as the independent variable, and the five beliefs in addiction will act

as the five moderators. If beliefs in any of the five models of addiction do significantly moderate

the relationship between the vignette conditions and public stigma, a simple slopes analysis will

be tested to interpret moderation effects (Cohen, Cohen, West & Aiken, 2014). Additionally, to

gain a greater understanding of how beliefs in each model relate to perceived stigma in the

addiction conditions specifically, a multiple regression will be run using SPSS where public

stigma is the dependent variable and beliefs in the five models of addiction are the independent

variable. This analysis will determine which models of addiction predict public stigma ratings

when participants respond to a vignette portraying addiction (i.e., the addiction and the co-

occurring addiction and mental health condition groups will be combined for this analysis).

Lastly, addressing the final hypothesis, a second univariate between-subjects ANOVA

will be conducted to determine whether there are mean differences in the acceptance and

endorsement of the BDMA based on geographical region. In this analysis, the dependent variable

is participants scores on the BDMA and the independent variable is the participants geographical

region (i.e. US, Canada or Australia). Tukey’s HSD post-hoc analysis will be performed if a

significant difference in acceptance of the BDMA, measured through the PAAS, is found based

on geographical region. Similarly, univariate between-subjects ANOVAs will be run for the

other models of addiction to determine whether there is a difference in the amount of acceptance


of these models by geographical region. Lastly, if there is evidence of a significant association of

geographical region with beliefs in any of the models of addiction, a mediation analysis will be

conducted to determine whether beliefs in these models of addiction mediate the relationship

between geographical region and public stigma ratings for individuals who specifically read the

vignettes of the addiction conditions. The dependent variable will be public stigma, the

independent variable will be geographical region (US or Canada) and the mediating variables

will be the models of addiction which resulted in a significant association in the previous

ANOVA. Indirect effects will be calculated and run by Andrew Hayes’s PROCESS v.3 (2013).

Results

Data were checked for validity/random responding and completeness. Upon collection,

the data were cleaned, and reverse scores were corrected. Subsequently, through SPSS, outliers

above and below 3 standard deviations were windsorized and after such, assumptions of

normality were tested, and results suggest they were not violated.

A univariate between-subjects ANOVA was run to determine if vignette condition

(addiction, mental health, co-occurring addiction mental health, and non-psychiatric medical

disorder) had an effect on public stigma ratings. There was a significant effect of vignette

condition on ratings of public stigma [F(3,868) = 47.18, p = .001; see Appendix E]. Post hoc

comparisons using Tukey HSD indicated that the mean stigma score for the addiction condition

(M = 51.91, SD = 10.98) significantly differed from the mean stigma score for the mental health

(M = 45.53, SD = 11.26) and non-psychiatric medical condition (M = 40.42, SD = 11.77).

Additionally, the co-occurring addiction and mental health condition (M = 50.31, SD = 10.76)

significantly differed from the mental health and non-psychiatric medical condition. Lastly, the

mean stigma score for the mental health condition significantly differed from the mean stigma


score for the non-psychiatric medical condition. However, there were no significant differences

between the addiction condition and the co-occurring addiction and mental health condition (see

Appendix F). Together this indicates that the addiction condition was the most stigmatized

condition followed by the co-occurring addiction and mental health condition, the mental health

condition, and the non-psychiatric medical condition, respectively.

A moderation model was run to determine if the effect of vignette condition on public

stigma was moderated by beliefs in the five models of addiction. Results suggest that the

psychological model of addiction, the nature model of addiction, and the sociological model of

addiction are significant moderators in the relationship between vignette condition and public

stigma [R2 = .48: F(3, 848) = 3.470, p = .016; F(3,848) = 3.365, p = 0.018; F(3,848) = 2.715, p =

0.044, respectively; see Appendix G. Subsequently, for each of the three models of addiction, a

test of simple slopes was run to determine which vignette conditions differ from each other.

Refer to Figures 1 – 3 (in Appendices H – J) to see the interaction effect of levels of public

stigma on vignette condition for the significant models of addiction.

Simple slopes analysis reveal that there are 2 significant differences between the slopes

of the vignette conditions when the psychological model of addiction moderates the relationship

between vignette condition and public stigma. The first significant difference was found between

the slopes of the addiction condition (β = -.063) and the non-psychiatric medical condition (β = -

.330). As beliefs in the psychological model of addiction increased, public stigma ratings

decreased more substantially for participants who viewed the non-psychiatric medical condition

vignette than participants who viewed the addiction condition vignette (β = 0.267, p = .002).

Similarly, a second significant difference between slopes was found between the addiction

condition and the co-occurring addiction and mental health condition (β = -.254). As beliefs in


the psychological model of addiction increased, public stigma ratings for participants in the co-

occurring addiction and mental health condition decreased more substantially than participants

who viewed the addiction condition (β = .191, p = .032; see Figure 1 in Appendix H and refer to

Appendix K for all simple slope comparisons for the psychological model of addiction

moderation).

For the nature model, simple slopes analysis reveal that there are 2 significant differences

between the slopes of the vignette conditions. The first significant difference was found between

the slopes of the addiction condition (β = -.100) and the mental health condition (β = .093). The

difference suggests that as beliefs in the nature model of addiction increased, public stigma

ratings for participants in the mental health condition increased whereas public stigma ratings for

participants in the addiction condition decreased (β = -.192, p = .012). The second significant

difference between vignette condition slopes was found between the addiction condition and the

non-psychiatric medical condition (β = .105). As beliefs in the nature model of addiction

increased, public stigma ratings for participants in the non-psychiatric medical condition

increased whereas public stigma ratings for participants in the addiction condition decreased (β =

-.204, p = .012; see Figure 2 in Appendix I and see Appendix L for all simple slope comparisons

for the nature model of addiction moderation).

Lastly, for the sociological model, simple slopes analysis revealed three significant

differences between the slopes of the vignette conditions. The first significant difference between

slopes was found between the addiction condition (β = -.022) and the co-occurring addiction and

mental health condition (β = .308). As beliefs in the sociological model of addiction increased,

public stigma ratings for participants in the co-occurring addiction and mental health condition

increased in comparison to public stigma ratings for the addiction condition (β = -.329, p = .041).


Additionally, the second significant difference between slopes was found between the mental

health condition (β = -.127) and the co-occurring addiction and mental health condition. As

beliefs in the sociological model of addiction increase, public stigma ratings increased for the co-

occurring addiction and mental health condition in comparison to the mental health condition (β

= -.435, p = .009). Lastly, and similarly, the third significant difference between slopes was

found between the non-psychiatric medical condition and the co-occurring addiction and mental

health condition (β = -.036). As beliefs in the sociological model of addiction increase, public

stigma ratings for the co-occurring addiction and mental health condition increased in

comparison to public stigma ratings for the non-psychiatric medical condition decreased (β = -

.344, p = .032). Overall, interpretations from Figure 3 in Appendix J suggest that as beliefs in the

sociological model of addiction increase, public stigma ratings for the co-occurring addiction and

mental health condition are significantly higher than the other three conditions (see Appendix M

for all simple slope for the sociological model of addiction moderation).

In order to increase power and to isolate effects to the conditions with addiction alone

(i.e. the addiction condition and the co-occurring addiction and mental health condition), a

multiple regression was run using SPSS to determine whether beliefs in the five models of

addiction predict public stigma for these two conditions. The results of the regression suggest

that our model explains 40% of the variance (R2 = .40, F(5,414)=54.98, p = .000). It was found

that three models of addiction were significant predictors of public stigma measured via the

PPPSM; the psychological model of addiction (β = -.147), the moral model of addiction (β =

.268) and the nature model of addiction (β = -.097; see Appendix N). Together this suggests that

greater acceptance of the moral model of addiction predicts greater stigma toward the

hypothetical individuals in the addiction related conditions and conversely, greater acceptance of


the psychological model of addiction and the nature model of addiction lower ratings of public

stigma toward the same hypothetical individuals in the addiction conditions.

Finally, to address the hypothesis that there would be greater acceptance of the BDMA in

the United States in comparison to Canada and Australia, a univariate between-subjects ANOVA

was computed to determine if there was greater acceptance of the BDMA based on geographical

region. Results supported a significant difference between the mean acceptance scores of the

BDMA based on geographical region [F(2,869) = 6.31, p = .002; See Appendix O]. Tukey’s

HSD post-hoc analysis suggested that the United States and Canada differ on their mean

acceptance ratings of the BDMA with MTurk respondents from the United States (M = 25.35,

SD = 6.11) endorsing greater acceptance of the model than Canada (M = 23.83, SD = 5.39). No

significant differences on mean acceptance ratings of the BDMA measured through the PAAS

were found between Australia (M = 23.86, SD = 7.09) and Canada or the United States (see

Appendix P).

Exploratory post-hoc analyses were run to explore the mean acceptance scores of the four

other models of addiction by geographical region. In order to reduce family-wise error rates, a

Bonferroni correction was used to determine a new significance level (.05/5 = .01). Results

suggest that there was a significant interaction between geographical region and beliefs in the

moral model of addiction [F(2,869) = 10.47, p = .000; see Appendix Q] where the United States

(M = 54.11, SD = 22.74) significantly differed from Canada (M = 46.52, SD = 19.25). No

significant differences were found between Australia (M = 53.44, SD = 25.29) and Canada or the

United States (see Appendix R). Additionally, results suggest that there were no significant

differences between mean belief scores of the psychological model of addiction [F(2,869) = .193,


p = .825], the nature model of addiction [F(2,869) = 2.13, p = .119], or the sociological model of

addiction [F(2,869) = 1.16, p = .314] based on geographical region.

Using PROCESS v.3 by Hayes (2013) two exploratory mediation models were then

conducted to determine whether beliefs in the BDMA and moral models of addiction mediate the

relationship between geographical region (US and Canada) and public stigma for the addiction

and co-occurring addiction and mental health conditions, only. Results suggest that the moral

model of addiction results in a significant indirect effect, with bootstrapped CI’s based on 5000

samples, on the relationship between geographical region and public stigma. The mediation

model indicates that greater endorsement of the moral model of addiction partly accounted for

the association between geographical region and public stigma with the US having greater

endorsement of the moral model of addiction which predicted higher ratings of public stigma (β

= -.430, BCa CI [-.850, -.052]; see Appendix S). The BDMA did not result in significant indirect

effects of the relationship between geographical region and public stigma (β = -.189, BCa CI [-

.464, .004).

Discussion

The current study had two main objectives: (1) to determine whether participants attribute

different levels of public stigma to the individuals in the vignette conditions depending on what

vignette condition they were randomized to, and whether their previous beliefs in the models of

addiction moderate that relationship and (2) to determine whether there is a difference in the (a)

amount of acceptance in the BDMA and the (b) amount of public stigma attributed to the

addiction related conditions based on geographical region, and whether this relationship is

mediated by beliefs in any of the models of addiction.


Objective one

Results from the current study suggest that addictive disorders are more stigmatized than

both a mental health and a non-psychiatric medical disorder. An individual with a co-occurring

addictive and mental health disorder is also significantly more stigmatized than an individual

with a mental health disorder and a non-psychiatric medical condition. Ultimately, this finding

supports the first hypothesis stating that a mental health disorder is less stigmatized than

addictive disorder. To our knowledge, the current study is the first to use a randomized design to

measure perceived stigma toward addictive disorders and other health conditions.

Moreover, the results did not suggest that there was a difference in mean stigma ratings

between an individual with addiction and an individual with a co-occurring addictive and mental

health disorder; individuals in the co-occurring addiction and mental health condition were not

less stigmatized than the individual in the addiction condition alone. Unfortunately, in our

society, 51% of individuals who report having a lifetime mental health disorder, also report

having at least one addictive disorder in their lifetime (Kessler et al., 1996). Future studies

should investigate the reasons why public stigma levels do not decrease when a mental health

disorder is present with an addictive disorder.

The second hypothesis aimed to determine whether an interaction exists between

participants’ beliefs in the BDMA and the four vignette conditions. Results suggest that

participants’ beliefs in the BDMA do not significantly moderate the relationship between

vignette condition and public stigma, yet beliefs in the psychological, nature, and sociological

models of addiction do. Consequently, the second hypothesis in the current study is not

supported which suggests that the claim that BDMA supporters have made stating that beliefs in

the BDMA will predict low public stigma scores toward the addictive population is not true. The


current study’s finding better supports non-BDMA supporters’ statements that “brain disease”

terminology will not predict low public stigma scores (Heather, 2016, Lewis, 2017, Davies,

2018) and that, rather, adoption of terms promoting addiction as a maladaptive coping strategy to

the individuals internal (psychological) and external (sociological) stressors or a biological

predisposition which has gone awry, may predict low public stigma scores.

Results from the moderation models suggest that high beliefs in the nature model of

addiction predicts lower ratings of public stigma ratings for the addiction vignette condition.

Further, greater beliefs in the psychological model of addiction also predicts lower public stigma

ratings for the co-occurring addiction and mental health condition whereas greater beliefs in the

sociological model of addiction predicts higher public stigma ratings for the co-occurring

addiction and mental health condition. This finding suggests that participants portrayed the

lowest stigma toward the co-occurring addiction and mental health condition when they believed

that addiction is caused by “maladaptive coping to internal stressors” and portrayed the highest

amount of stigma when they believed addiction is caused by “maladaptive coping to external

stressors.” Further, it may possibly be true that the reason why the psychological model of

addiction predict low public stigma ratings toward this population is due to the fact that these

individuals suffer from a mental health disorder. For instance, in this study, the mental health

disorder was depression, a disorder which is manifested and perpetuated by negative thoughts

(e.g. obsessive rumination). Consequently, predicted public stigma may be low for the

population when beliefs in the psychological model of addiction are high because the

participants believe that the individual is using alcohol (the substance representing the addictive

disorder in the condition) as a way to cope with their depressive disorder. Overall, their co-


occurring addictive and mental health condition is due to the fact that they have developed a

maladaptive coping strategy to the internal stressor of their mental health disorder.

It is also interesting to note which models of addiction predict low public stigma ratings

for both the addiction vignette condition and the co-occurring addiction and mental health

vignette. Although both conditions include an addictive disorder, not one model of addiction

from the moderation models seems to significantly predict low public stigma ratings for both

conditions. That being said, results from the multiple regression in the current study suggest that

greater acceptance of the moral model of addiction predicted greater public stigma towards the

addiction vignette condition and the co-occurring addiction and mental health vignette condition.

This means that people who attribute blame to individuals suffering from addiction for their

problems hold higher levels of stigma towards these individuals. Conversely, participants with

greater acceptance of the psychological and nature models of addiction showed less levels of

public stigma to those suffering from addiction. This finding suggests that participants who

believe that those with substance use disorders are unable to cope with their internal stressors

and participants who believe that addiction is a natural predisposition that has gone awry, tend to

have greater sympathy for the hypothetical individual and therefore hold less stigma toward

them. After the multiple regression, these findings still do not support the second hypothesis in

the current study which stated that individuals with higher acceptance of the BDMA will have

significantly lower ratings of public stigma to individuals with addiction.

This finding is inconsistent with views and statements made by supporters of the BDMA.

Supporters of the BDMA note that with greater acceptance of the BDMA, less stigma will be

attributed to those suffering from addictive disorders and in turn there will be greater treatment

seeking from the addictive population (Volkow, Koob & McLellan, 2016). The current study’s


finding is more consistent with non-BDMA supporters who suggest that “disease terminology,”

which drives the BDMA, will not decrease stigma (Lewis, 2017; Heather, 2017). Although the

current study does not support the statement that acceptance of the BDMA increases stigma, it

does support the claim that is does not decrease stigma.

Overall, this finding supports a nature model of addiction and/or a psychological model

of addiction over the other three models of addiction measured. Consequently, this would mean

that low public stigma ratings are associated with high beliefs in addiction being defined as

either an abnormal reaction to the innate drive the human species has to alter their state of

consciousness or poor coping strategies for handling stressors felt by individuals,

psychologically.

Currently, there is no literature which has specifically looked at the nature model of

addiction in regard to its perspective on the etiology, progression, and treatment of addiction.

Though, since the nature model of addiction emphasizes that addiction is a maladaptive response

to a natural drive to use substances of abuse, it is clear why greater beliefs in the nature model of

addiction may reduce public stigma; using substances of abuse is not abnormal behavior, it is a

regular behavior that happens frequently in our society. Unfortunately, some individuals have an

abnormal reaction to using substances of abuse and develop addiction; it is not the individual’s

fault, and consequently, less stigma is attributed to that individual. Additionally, although the

nature model of addiction seems to stand on its own, it may also be argued that the nature model

of addiction could possibly be a bridge between the BDMA and other models of addiction since

the nature model of addiction truly stems from an innate biological predisposition to addiction.

Moreover, the nature model of addiction asserts that every race in the human species is

subjected to this innate drive and has been since the beginning of time. For instance, the use of


the amanita muscaria mushroom, commonly known as fly acaria, has been a part of religious

rituals in Central Asia for over 4000 years (Croqu, 2007). Similarly, some of the oldest wine

vineyards were in Georgia, US and are dated back to between 7000 to 5000 BC (Croqu, 2007).

Consequently, history indicates that this innate drive to alter our consciousness may not only be

present today but may be a part of our genetic makeup as a species.

The psychological model of addiction, although not used to define what addiction is, is

stated as a prevalent reason why individuals choose to use substances. For instance, it is well

known that one of the qualities that deems an individual at-risk for addiction is using substances

to be able to cope with issues that are in their life (Craske, 2017). Treatment programs and

counseling services alike are designed to modify individuals coping strategies, and change the

way that individuals handle their urges to use substances as an outlet (Craske, 2017). Overall,

results from this study seem to suggest that the greater the public believes in the psychological

model of addiction, no matter their geographical region, the less likely they are to stigmatize

individuals who suffer from an addictive disorder or a co-occurring mental health and addictive

disorder. Consequently, participants attributed less stigma toward both the addictive and the

addictive and mental health population when they believed that the hypothetical individual used

substances of abuse to cope with their own internal struggles.

Objective two

Results from the current study indicate that participants from the United States had

significantly greater acceptance of the BDMA and the moral model of addiction in comparison to

the Canadian sample. This finding supports our final hypothesis stating that there would be

greater acceptance of the BDMA in the United States in comparison to the other countries. It is

important to note that no significant differences were found between Australia and the other two


countries and this is presumed to be due to the small sample size from Australia (this is discussed

in more detail below). Similarly, it is also imperative to comment on the fact that these results

reflect mean ratings of acceptance of the BDMA and do not indicate that all individuals from the

United States accept and endorse a BDMA. It may be possible that the reason why the BDMA

has a higher acceptance rate in the United States in comparison to Canada is due to the

proponents of the BDMA residing and promoting a BDMA through their work within the

country.

The exploratory mediation models completed in the current study suggest that only the

moral model of addiction had an effect on the relationship between geographical region and

public stigma where the US had higher endorsement rates of the moral model which increased

public stigma ratings towards individuals with addiction. Even with evidence to support that

there is significantly greater acceptance of the BDMA in the US in comparison to Canada, this

acceptance did not have any effect on public stigma ratings; acceptance of the BDMA did not

significantly increase nor decrease public stigma ratings. These findings support Lewis (2017)

and Heather’s (2016) statements that brain disease terminology does not reduce public stigma.

Consequently, the approaches to reduce stigma in our society by terming addiction a brain

disease is not effective and other approaches should be taken to reduce the stigma felt by the

addictive population.

Limitations and Future Research

The findings from the current study should be taken in light of the study’s limitations. For

instance, the sample size of participants from Australia (N = 50) was small in comparison to the

other countries where data was collected [Canada (N = 248), and the United States (N = 574)].

One of the main objectives of this study was to determine whether there were regional


differences in the acceptance of the BDMA. Unfortunately, due to the small sample size from

Australia, low power was observed and consequently, there was a loss in the ability to identify

significant differences between our geographical regions.

Another major limitation was the PAAS which was the measure used to quantify beliefs

in the five models of addiction. This measure includes a disease model of addiction subscale, not

specifically a BDMA subscale. Although the items in the disease subscale touch upon the main

aspects of the BDMA, since the two models are synonymous, the subscale could be more

extensive and include more aspects of the BDMA. Indeed, there has been a significant amount of

research conducted to examine the BDMA and, as such, there is opportunity to create more

relevant items to measure beliefs and acceptance of the model. It is also important to note that

although the disease subscale in this measure has a Cronbach’s α = .703, it only includes 6 items.

Thus, future research should develop a measure to quantify more comprehensively individual’s

beliefs and acceptance of the BDMA. Once a measure is developed it could be used to identify

the implications of many different types of individuals beliefs of the BDMA, including

clinician’s and treatment providers. As such, research could identify whether greater acceptance

of the BDMA influences clinical decision making in regard to treatment plans for patients, and

whether a clinician may change their treatment plans for a patient due to their acceptance and

endorsement of the BDMA.

In addition, in light of the current study’s findings, it may also be important to develop

more extensive measures for quantifying individuals’ beliefs toward both the nature model of

addiction and the psychological model of addiction. The reason behind this would be to explore

other implications of these beliefs on the population of those with substance use disorders (i.e.

besides public stigma). For instance, since the current study’s findings suggest the nature and


psychological models of addiction predict lower ratings of public stigma for the population of

those with substance use disorders, future research should further investigate how beliefs in these

models of addiction relate to clinical decision-making and the treatment selection process for

clinicians.

The current study measured stigma towards the hypothetical individuals in the vignette

conditions by measuring public stigma. Future research should also determine whether self-

stigma (i.e. when an individual becomes aware of the stereotypes attributed to them and

internalizes them) is reduced when addiction is defined by these five models of addiction. If

beliefs in the nature and psychological models of addiction were also shown to reduce self-

stigma in the addictive population, it would further support the current study’s findings that in

order to reduce stigma toward the addictive population, these models of addiction must be

promoted and accepted in society. That said, these studies can also be replicated in samples of

families and friends of those with a history of addiction. If studies continue to show that beliefs

in the nature model of addiction and the psychological model of addiction are associated with

lower stigma scores, it may suggest that these models should be promoted in society.

The current study also lacked data on participant’s past experience with addiction. For

instance, it is very likely that at least some of the participants in the current study have suffered

from an addictive disorder and/or know of someone (a family member or friend) who has

suffered from an addictive disorder. Having past exposure to addiction may critically impact that

individual’s beliefs in the five models of addiction and the level of public stigma they may

attribute to the hypothetical individual in the vignette condition. Future research should collect

this data to be able to analyze whether past exposure to an addictive disorder impacts the


participants beliefs in the models of addiction and the level of stigma they attribute toward the

vignette conditions.

Another suggestion for future research is to determine whether a decrease in public

stigma felt by a population of those with substance use disorders would truly in turn increase

treatment seeking. Although it has been argued by Volkow and her colleagues at NIDA that it

would, there is currently no empirical evidence in fact demonstrating that treatment seeking will

increase with less public stigma (Volkow, Koob & McLellan, 2016). Further, future research

may also determine whether a reduction in self-stigma increases treatment seeking from the

addictive population. It may be interesting to determine whether public stigma or self-stigma has

a greater impact on treatment seeking behavior. For instance, it may be possible that self-stigma

has a greater impact on treatment seeking behavior in comparison to public stigma as it is that

individual who must make the decision to seek treatment and if they do not internalize the

stereotypes developed by the public, they may be more likely to seek treatment than another

individual who holds a higher level of self-stigma. Further, it may be true that self-stigma

mediates the relationship between public stigma and treatment seeking where as public stigma

reduces, so does self-stigma which directly increases treatment seeking from the addictive

population. This could possibly be true since self-stigma is defined as a product of public stigma.

In conclusion, the current study suggests that public stigma is lowest toward individuals

with substance use disorders when addiction is defined as either a psychological disorder or a

natural disorder; it is not best defined as a brain disease. Future research should focus around

these two models of addiction, determine their societal implications, level of acceptance, and

whether they would promote treatment seeking amongst a population of those with substance use

disorders.


References

Aubin, H. J., & Daeppen, J. B. (2013). Emerging pharmacotherapies for alcohol dependence: a

systematic review focusing on reduction in consumption. Drug and alcohol

dependence, 133(1), 15-29. doi:10.1016/j.drugalcdep.2013.04.025.

Barnett, A. I., Hall, W., Fry, C. L., Dilkes‐Frayne, E., & Carter, A. (2018). Drug and alcohol

treatment providers’ views about the disease model of addiction and its impact on clinical

practice: A systematic review. Drug and alcohol review, 37(6), 697-720.

doi:10.1111/dar.12632.

Barnett, A. I., & Fry, C. L. (2015). The clinical impact of the brain disease model of alcohol and

drug addiction: exploring the attitudes of community-based AOD clinicians in

Australia. Neuroethics, 8(3), 271-282. doi:10.1007/s12152-015-9236-5.

Bell, S., Carter, A., Mathews, R., Gartner, C., Lucke, J., & Hall, W. (2014). Views of addiction

neuroscientists and clinicians on the clinical impact of a ‘brain disease model of

addiction’. Neuroethics, 7(1), 19-27. doi:10.1007/s12152-013-9177-9.

Berridge, K. C. (2017). Is addiction a brain disease?. Neuroethics, 10(1), 29-33.

doi:10.1007/s12152-016-9286-3.

Brickman, P., Rabinowitz, V. C., Karuza, J., Coates, D., Cohn, E., & Kidder, L. (1982). Models

of helping and coping. American psychologist, 37(4), 368-384. doi:10.1037/0003

066X.37.4.368.

Broadus, A. D., & Evans, W. P. (2014). Developing the public attitudes about addiction

instrument. Addiction Research & Theory, 23(2), 115-130.

doi:10.3109/16066359.2014.942296.


Buchman, D. Z., Illes, J., & Reiner, P. B. (2011). The paradox of addiction

neuroscience. Neuroethics, 4(2), 65-77. doi:10.1007/s12152-010-9079-z.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2014). Applied multiple regression/correlation

analysis for the behavioral sciences. Psychology Press.

Colliver, J. D., Compton, W. M., Gfroerer, J. C., & Condon, T. (2006). Projecting drug use

among aging baby boomers in 2020. Annals of epidemiology, 16(4), 257-265. doi:

10.1016/j.annepidem.2005.08.003.

Craske, M. G. (2017). Cognitive-behavioral therapy (2nd ed. Ed.) American Psychological

Association, Washington, DC. doi:10.1037/0000027-000.

Crocq M. A. (2007). Historical and cultural aspects of man's relationship with addictive

drugs. Dialogues in clinical neuroscience, 9(4), 355–361.

Davies, J. (2018). Addiction is not a brain disease. doi: 10.1080/16066359.2017.1321741.

Donny, E. C., Walsh, S. L., Bigelow, G. E., Eissenberg, T., & Stitzer, M. L. (2002). High-dose

methadone produces superior opioid blockade and comparable withdrawal suppression to

lower doses in opioid-dependent humans. Psychopharmacology, 161(2), 202-212.

doi:10.1007/s00213-002-1027-0.

Fenton, T., & Wiers, R. W. (2017). Free will, black swans and addiction. Neuroethics, 10(1),

157-165. doi:10.1007/s12152-016-9290-7.

Gage, S. H., & Sumnall, H. R. (2019). Rat park: How a rat paradise changed the narrative of

addiction: (alcoholism and drug addiction). Addiction, 114(5), 917-922.

doi:10.1111/add.14481.


Hall, W., & Carter, A. (2013). Anticipating possible policy uses of addiction neuroscience

research. Drugs: Education, Prevention and Policy, 20(3), 249-257.

doi:10.3109/09687637.2013.779426.

Hall, W., Carter, A., & Barnett, A. (2017). Disease or developmental disorder: competing

perspectives on the neuroscience of addiction. Neuroethics, 10(1), 103-110.

doi:10.1007/s12152-017-9303-1.

Hall, W., Carter, A., & Forlini, C. (2015). The brain disease model of addiction: is it supported

by the evidence and has it delivered on its promises? The Lancet Psychiatry, 2(1), 105

110. doi:10.1016/S2215-0366(14)00126-6.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A

regression-based approach, Guilford Press, New York, NY.

Heather, N. (2017). Q: Is addiction a brain disease or a moral failing? A:

Neither. Neuroethics, 10(1), 115-124. doi:10.1007/s12152-016-9289-0.

Heather, N., Best, D., Kawalek, A., Field, M., Lewis, M., Rotgers, F., Weirs, R., & Heim, D.

(2018). Challenging the brain disease model of addiction: European launch of the

addiction theory network. doi:10.1080/16066359.2017.1399659.

Heather, N., & Segal, G. (Eds.). (2016). Addiction and choice: rethinking the relationship.

Oxford University Press.

Heyman, G. M. (2013). Addiction and choice: Theory and new data. Frontiers in Psychiatry, 4,

5.


Holman, D. (2015). Exploring the relationship between social class, mental illness stigma and

mental health literacy using British national survey data. Health: An Interdisciplinary

Journal for the Social Study of Health, Illness and Medicine, 19(4), 413-429.

doi:10.1177/1363459314554316.

Humphreys, K., Greenbaum, M. A., Noke, J. M., & Finney, J. W. (1996). Short understanding of

substance abuse scale. doi:10.1037/t02615-000.

Kelly, J. F. (2018). EM Jellinek’s Disease Concept of Alcoholism. Addiction.

doi:10.1111/add.14400.

Kessler, R. C., Nelson, C. B., McGonagle, K. A., Edlund, M. J., Frank, R. G., & Leaf, P. J.

(1996). The epidemiology of co-occurring addictive and mental disorders: Implications

for prevention and service utilization. American Journal of Orthopsychiatry, 66(1), 17

31. doi:10.1037/h0080151.

Klingemann, H., Sobell, M. B., & Sobell, L. C. (2010). Continuities and changes in self‐change

research. Addiction, 105(9), 1510-1518.

Kloss, J. D., & Lisman, S. A. (2003). Clinician attributions and disease model perspectives of

mentally ill, chemically addicted patients: A preliminary investigation. Substance use &

misuse, 38(14), 2097-2107. doi:10.1081/JA-120025127.

Kvaale, E. P., Haslam, N., & Gottdiener, W. H. (2013). The ‘side effects’ of

medicalization: A meta-analytic review of how biogenetic explanations affect stigma.

Clinical Psychology Review, 33, 782-794. doi:10.1016/j.cpr.2013.06.002.

Lebowitz, M. S., Pyun, J. J., & Ahn, W. (2014). Biological explanations of generalized anxiety

disorder: Effects on beliefs about prognosis and responsibility. Psychiatric Services,

65(4), 498-503. doi:10.1176/appi.ps.201300011.


Leshner, A. I. (1997). Addiction is a brain disease, and it matters. Science, 278(5335), 45-47.

doi:10.1126/science.278.5335.45.

Levy, N. (2013). Addiction is not a brain disease (and it matters). Frontiers in Psychiatry, 4, 24.

doi:10.3389/fpsyt.2013.00024.

Lewis, M. (2018). Brain Change in Addiction as Learning, Not Disease. New England Journal of

Medicine, 379(16), 1551-1560. doi:10.1056/NEJMra1602872.

Lewis, M. (2017). Addiction and the brain: development, not disease. Neuroethics, 10(1), 7-18.

doi:10.1007/s12152-016-9293-4.

Link, B. G., Phelan, J. C., Bresnahan, M., Stueve, A., & Pescosolido, B. A. (1999). Public

conceptions of mental illness: Labels, causes, dangerousness, and social distance.

American Journal of Public Health, 89(9), 1328-33. doi:10.2105/AJPH.89.9.1328.

Meurk, C., Morphett, K., Carter, A., Weier, M., Lucke, J., & Hall, W. (2016a). Scepticism and

hope in a complex predicament: People with addictions deliberate about neuroscience.

International Journal of Drug Policy, 32, 34-43. doi:10.1016/j.drugpo.2016.03.004.

Meurk, C., Fraser, D., Weier, M., Lucke, J., Carter, A., & Hall, W. (2016b). Assessing the place

of neurobiological explanations in accounts of a family member’s addiction. Drug and

Alcohol Review, 35(4), 461-469. doi:10.1111/dar.12318.

Meurk, C., Partridge, B., Carter, A., Hall, W., Morphett, K., & Lucke, J. (2014). Public attitudes

in Australia towards the claim that addiction is a (brain) disease. Drug and alcohol

review, 33(3), 272-279. doi:10.1111/dar.12115.

Miller, W. R. (1987). Motivation and treatment goals. Drugs & Society, 1(2-3), 133-151. doi:

10.1300/J023v01n02_06.


Miller, N. S., & Giannini, A. J. (1990). The Disease Model of Addiction: A Biopsychiatrist's

View. Journal of psychoactive drugs, 22(1), 83-85.

doi:10.1080/02791072.1990.10472201.

Monti, P. M., Kadden, R. M., Rohsenow, D. J., Cooney, N. L., & Abrams, D. B.

(2002). Treating alcohol dependence: A coping skills training guide (2nd ed.)

Guilford Press, New York, NY.

Morphett, K., Carter, A., Hall, W., & Gartner, C. (2017). Framing tobacco dependence as a

“brain disease”: Implications for policy and practice. Nicotine & Tobacco

Research, 19(7), 774-780. doi:10.1093/ntr/ntx006.

Mosher, C. J., & Akins, S. (2007). Drugs and drug policy: The control of consciousness

alteration. Sage.

Moyers, T. B., & Miller, W. R. (1993). Therapists' conceptualizations of alcoholism:

Measurement and implications for treatment decisions. Psychology of Addictive

Behaviors, 7(4), 238-245. doi:10.1037/0893-164X.7.4.238.

Nace, E. P. (2015). In Galanter M., Kleber H. D. and Brady K. T. (Eds.), History of alcoholics

anonymous and the experience of patients (5th ed. Ed.) American Psychiatric Publishing,

Inc., Arlington, VA.

Philip, B., Vita Carulli, R., Jurgis, K., Jr., Dan, C., Ellen, C., & Louise, K. (1982). Models of

helping and coping. American Psychologist, 37(4), 368-384. doi:10.1037/0003

066X.37.4.368.


Riper, H., Andersson, G., Hunter, S. B., de Wit, J., Berking, M., & Cuijpers, P. (2014).

Treatment of comorbid alcohol use disorders and depression with cognitive‐behavioural

therapy and motivational interviewing: A meta‐analysis. Addiction, 109(3), 394-406.

doi:10.1111/add.12441.

Room, R., Rehm, J., Trotter, R. T., Paglia, A., & Ustun, T. B. (2001). Cross-Cultural Views on

Stigma, Valuation, Parity, and Societal Values Towards Disability. In B.T Üstün, S.

Chatterii, J.E Bickenbach, R.T, Trotter & R. Room (Eds.), Disability and Culture:

Universalism and diversity (pp. 247-291). Ashland, OH: Hogrefe & Huber Publish.

Rush, B. (1812). Medical inquiries and observations upon the diseases of the mind. Kimber,

Richardson.

Russell, C., Davies, J. B., & Hunter, S. C. (2011). Predictors of addiction treatment providers’

beliefs in the disease and choice models of addiction. Journal of Substance Abuse

Treatment, 40(2), 150-164. doi:10.1016/j.jsat.2010.09.006.

Satel, S. L., & Lilienfeld, S. O. (2017). If addiction is not best conceptualized a brain disease,

then what kind of disease is it? Neuroethics, 10(1), 19-24. doi:10.1007/s12152-016

9287-2.

Schaler, J. A. (1995). The addiction belief scale. International Journal of the Addictions, 30(2),

117-134. doi:10.3109/10826089509060737.

Schaler, J. A. (2011). Addiction is a choice. Open Court Publishing Co, Chicago, IL.

Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36(2), 241-263.

doi:10.1016/S0896-6273(02)00967-4.


Snoek, A. (2017). How to recover from a brain disease: Is addiction a disease, or is there a

disease-like stage in addiction? Neuroethics, 10(1), 185-194. doi:10.1007/s12152-017

9312-0.

Snoek, A., & Matthews, S. (2017). Introduction: Testing and Refining Marc Lewis’s Critique of

the Brain Disease Model of Addiction. Neuroethics, 10(1), 1-6. doi:10.1007/s12152-017

9310-2.

Stapleton, J., West, R., Hajek, P., Wheeler, J., Vangeli, E., Abdi, Z., . . . Sutherland, G. (2013).

Randomized trial of nicotine replacement therapy (NRT), bupropion and NRT plus

bupropion for smoking cessation: Effectiveness in clinical practice. Addiction, 108(12),

2193-2201. doi:10.1111/add.12304.

Vrecko, S. (2010). Birth of a brain disease: Science, the state and addiction

neuropolitics. History of the Human Sciences, 23(4), 52-67.

doi:10.1177/0952695110371598.

Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain

disease model of addiction. New England Journal of Medicine, 374(4), 363-371.

doi:10.1056/NEJMra1511480.

Volkow, N., & Morales, M. (2015). The brain on drugs: from reward to addiction. Cell, 162(4),

712-725. doi:10.1016/j.cell.2015.07.046.


Appendix A

Demographic Form

Q1. What is your birth sex? o Male o Female

Q2.Howoldareyouinyears?________Q3.Whatracedoyouconsideryourselftobe(selectoneormoreofthefollowing)?

o White/Caucasian (European) o African descent/African o East or Southeast Asian (e.g. China, Korea) o South Asian (e.g. Indian, Pakistan, Sri Lanka) o Middle Eastern o Latin, Central, and South American o Caribbean o Pacific Island o Aboriginal o Other - If“Other”,pleasedescribe:________

Q4.Wheredoyoucurrentlylive? o Canada o The United States o Australia

Q5.Whatisthehighestlevelofeducationthatyouhavecompleted?

Q7. Are you employed? (you may select more than one)

o Full time o Part time o Student o Unemployed/not currently working

Q8.Whatisthecurrencydoyougetpaidwith? o CAD o USD o AUD

Q9.Pleaseinputyourhouseholdyearlyincomeinyourpreviouslychosencurrency________

(Displayed when “Canada” or “The United States” is selected for Q4)

• Less than high school • High school diploma or GED • Some college • Bachelors degree • Masters degree • Doctoral degree • Associated degree or technical

certification

(Displayed when “Australia” is selected for Q4)

• Less than senior secondary school • Senior Secondary school diploma • Some higher school • Bachelors degree • Masters degree • Doctoral degree • Vocational education or technical

certification


Appendix B

Vignette Manipulations Alcohol Use Disorder Version For the past several months, John/Jane has been suffering from tiredness, low energy and difficulty carrying out his daily routines. Several times, he/she has tried to motivate himself/herself but continues to have difficulty keeping up with his/her work and family obligations. Recently, the only thing that has made John/Jane feel better is drinking alcohol. John’s/Jane’s wife/husband has noticed his/her behaviour becoming worse over the last few months and urges him/her to go to a doctor. At his/her doctor’s visit, John/Jane is diagnosed with an alcohol use disorder (a dependence on alcohol). The doctor tells John/Jane that this is potentially a long-term condition that could get worse over time, but that John’s/Jane’s condition could also improve if he/her starts treatment now. Depression Version For the past several months, John/Jane has been suffering from tiredness, low energy and difficulty carrying out his/her daily routines. Several times, he/she has tried to motivate himself/herself but continues to have difficulty keeping up with his/her work and family obligations. Recently, the only thing that has made John/Jane feel better is avoiding work and social activities. John’s/Jane’s wife/husband has noticed his/her behaviour becoming worse over the last few months and has urged him/her to go to a doctor. At his/her doctor’s visit, John/Jane was diagnosed with clinical depression. The doctor tells John/Jane that this is potentially a long-term condition that could get worse over time, but that John’s/Jane’s condition could also improve if he/she starts treatment now. Depression and Alcohol Use Disorder Version For the past several months, John/Jane has been suffering from tiredness, low energy and difficulty carrying out his/her daily routines. Several times, he/she has tried to motivate himself/herself but continues to have difficulty keeping up with his/her work and family obligations. Recently, the only things that have made John/Jane feel better is avoiding work and social activities, and drinking alcohol. John’s/Jane’s wife/husband has noticed his/her behaviour becoming worse over the last few months and has urged him/her to go to a doctor. At his/her doctor’s visit, John/Jane was diagnosed with clinical depression and an alcohol use disorder (a dependence on alcohol). The doctor tells John/Jane that these are potentially long-term conditions that could get worse over time, but that John’s/Jane’s conditions could also improve if he/she starts treatment now. Diabetes Version For the past several months, John/Jane has been suffering from tiredness, low energy and difficulty carrying out his/her daily routines. Several times, he/she has tried to motivate himself/herself but continues to have difficulty keeping up with his/her work and family obligations. Recently, the only thing that has made John/Jane feel better is eating food, especially sugary foods. John’s/Jane’s wife/husband has noticed his/her behaviour becoming worse over the last few months and has urged him/her to go to a doctor. At his/her doctor’s visit, John/Jane was diagnosed with diabetes. The doctor tells John/Jane that this is potentially a long-term condition that could get worse over time, but that John’s/Jane’s condition could also improve if he/she starts treatment now.


Appendix C

Personal and Perceived Public Stigma Measures

Type of stigma/questions Response categories Subscale 1: Perceived public stigma People like John should feel embarrassed about their situation 1 ‘Strongly disagree’ People like John should feel afraid to tell others about their situation. 2 ‘Disagree’ John has little hope of ever being accepted into the community 3 ‘Agree’ Members of John’s family would be better off if their situation was kept 4 ‘Strongly agree’ secret Subscale 2: Perceived treatment stigma Getting treatment would make John an outsider in the community 1 ‘Strongly disagree’ If people knew John was in treatment, John would lose friends 2 ‘Disagree’ Opportunities would be limited if people knew John received treatment 3 ‘Agree’ 4 ‘Strongly agree’ Subscale 3: Personal stereotypical/prejudicial stigma How likely is it John would do something violent to others 1 ‘Not at all likely’ How likely is it John would do something violent to others to themselves 2 ‘Not very likely’ People like John are just as intelligent as anyone else (r) 3 ‘Somewhat likely’ People like John are more creative than others 4 ‘Very likely’; People like John who have jobs are just as productive as others (r) 1 ‘Strongly disagree’ People like John are unpredictable 2 ‘Disagree’ People like John are just as trustworthy as anyone else (r) 3 ‘Agree’ People like John are hard to talk to 4 ‘Strongly agree’ Being around John would make me feel uncomfortable Being around John would make me feel nervous Subscale 4: Personal discriminatory stigma I would be willing to have John as a neighbour (r) 1 ‘Definitely willing’ I would be willing to socialise with John (r) 4 ‘Definitely unwilling' I would be willing to have John care for my children (r) I would be willing to befriend John (r) I would be willing to work with John (r) I would be willing to have John marry someone I know (r)


Appendix D Public Attitudes About Addiction Survey (PAAAS)

(Broadus & Evans, 2014) Psychology Model P1. Traumatic events may lead to addiction. P2. An inability to gain pleasure from life may lead to addiction. P3. Individuals engage in risky behaviours that might lead to addiction, because they are depressed. P4. Addicts use to escape from bad family situations. P5. Individuals engage in risky behaviours that might lead to addiction, because they are avoiding personal problems. P6. An addict continues to use even when they know the cost of their behaviour. P7. A person can be addicted to anything from drugs to video games. P8. Individuals engage in risky behaviours that might lead to addiction, because they lack self-confidence. P9. Individuals engage in risky behaviours that might lead to addiction, in order to feel better about themselves. P10. What causes addiction? Children who lack emotional support may choose to use drugs as an adult. P11. Even in religious communities, there are addicts. P12. Anyone can become an addict. P13. What causes addiction? Pain can cause addiction. P14. What causes addiction? Addiction is caused by unhappiness in a person’s life, marriage, or job. P15. What causes addiction? The instant reward a person feels from certain behaviours leads to addiction. Moral Model M16. Addicts lack moral standards. M17. Addicts are low life people. M18. Addicts are failures. M19. Addicts are immature people. M20. Addicts have a carefree attitude towards life. M21. If an addict fails to recover in treatment, it is because they are not motivated to quit. M22. You can tell a person is an addict by their appearance. M23. It is easy to tell if someone has an addiction. M24. Addiction is best seen as a habit, not as a disease. M25. Saying that addiction is a disease implies a lack of personal responsibility. M26. Addiction is a choice M27. It is their own fault if an addict relapse. M28. Individuals engage in risky behaviours that might lead to addiction, because they do not respect authority.


M29. Addiction is a form of wrongdoing. M30. Poor people are less motivated to obey laws about risky behaviours like drug use. M31. Although addictive behaviour is a choice, the person is influenced in that choice by their moral values. Nature Model N32. Daily use of small amounts of substances like marijuana is not necessarily harmful. N33. Marijuana is accepted in some communities, so there is nothing wrong with using it while there. N34. Personal use of drugs should be legal in the confines of one’s own home. N35. As long as no one else is harmed, people should have the right to engage in whatever behaviours they want. N36. Some people use drugs, but never become addicted. N37. Addiction does not always result in a negative outcome. N38. People fail to consider that some addictive behaviours may be positive. N39. People often outgrow drug and alcohol addiction. N40. There are people who have significant problems with alcohol, but who are not alcoholics. N41. Addicts can learn to control their use. Sociology Model S42. What factors influence attitudes about addiction? Beliefs about addiction S43. What factors influence attitudes about addiction? Religious beliefs S44. A person’s culture influences their attitudes toward addiction. S45. What causes addiction? If a person’s neighbourhood supports drug use, a person is more likely to use drugs. S46. What factors influence attitudes about addiction? A person’s environment. S47. What factors influence attitudes about addiction? The media (e.g. news, television, movies, etc.). S48. Although risky behaviour is a choice, the person is influenced in that choice by their upbringing and education. Disease Model D49. Addicts cannot control their addictive behaviour. D50. Addicts cannot use pain medicine. They would become addicted to it. D51. Addicts are not capable of solving their addiction on their own. D52. What causes addiction? Genetics not psychology, determines whether one drinker will become addicted to alcohol and another will not. D53. Drug use changes the brain after a few exposures and causes addiction. D54. ‘‘Once an addict, always an addict’’ is a true statement.

*Participants responses based on a 7-item scale: 1 (Strongly Disagree), 2 (Disagree), 3 (Somewhat Disagree), 4 (Neither Disagree nor Agree), 5 (Somewhat Agree), 6 (Agree) and 7

(Strongly Agree).


Appendix E

Table 1.

Univariate Between-Subjects ANOVA Analyzing Mean Differences in Public Stigma given

Participants Vignette Condition.

Predictor Sum of Squares df Mean

Square F p partial h2

(Intercept) 1917989.66 1 1917989.66 15250.95 .000 .946

Condition 17799.05 3 5933.02 47.177 .000 .140

Error 109161.436 868 125.76


Appendix F

Table 2.

Tukey HSD Post-Hoc Analysis on ANOVA in Table 1.

95% CI

Condition Other Conditions

Mean Difference Std. Error Sig. Lower

Bound Upper Bound

ADD MH 6.38* 1.048 .000 3.68 9.08 ADD/MH 1.61 1.099 .462 -1.22 4.44 M 11.49* 1.053 .000 8.78 14.20

MH ADD -6.38* 1.048 .000 -9.08 -3.6 ADD/MH -4.77* 1.102 .000 -7.61 -1.94 M 5.11* 1.055 .000 2.39 7.82

ADD/MH ADD -1.61 1.099 .462 -4.44 1.22 MH 4.77* 1.102 .000 1.94 7.61 M 9.88* 1.106 .000 7.03 12.73

M ADD -11.49* 1.053 .000 -14.20 -8.78 MH -5.11* 1.055 .000 -7.82 -2.39 ADD/MH -9.88* 1.106 .000 -12.73 -7.03

Note: ADD = Addiction; MH = Mental Health; ADD/MH = Co-Occurring Addiction and Mental

Health; M = Non-Psychiatric Medical Disorder


Appendix G

Table 3.

Significant Moderators of the Relationship Between Vignette Condition and Public Stigma

Interaction Between Condition and Model of

Addiction

Sum of Squares df Mean


Condition*Psychological 788.71 3 262.90 3.47 .016 .012

Condition*Moral 218.72 3 72.91 .962 .410 .003

Condition*Nature 764.83 3 254.95 3.37 .018 .012

Condition*Sociological 617.20 3 205.73 2.72 .044 .010

Condition*Disease 293.34 3 97.70 1.29 .276 .005

Error 64250.42 848 75.77


Appendix H

Figure 1.

Beliefs in the Psychological Model of Addiction Moderating the Effect Between Vignette

Condition and Public Stigma.

Note. Low and high stigma scores are those on the first quartile on either side of the mean.

0

5

10

15

20

25

30

35

40

45

50

Low (-1SD) Mean High (+1SD)

Publ

ic S

tigm

a

Belief in Psychological Model of Addiction

Belief in the Psychological Model of Addiction Moderating the Effect Between Vignette Condition and Public Stigma

Addiction

Mental Health

Addiction/Mental Health

Medical


Appendix I

Figure 2.

Beliefs in the Nature Model of Addiction Moderating the Effect Between Vignette Condition and

Public Stigma.


0

10

20

30

40

50

60


Publ

ic S

tigm

a

Beliefs in the Nature Model of Addiction

Beliefs in the Nature Model of Addiction Moderating the Effect Between Vignette Condition and Public Stigma

Addiction

Mental Health


Medical


Appendix J

Figure 3.

Beliefs in the Sociological Model of Addiction Moderating the Effect Between Vignette Condition

and Public Stigma.


0

10

20

30

40

50

60

70


Publ

ic S

timga

Beliefs in the Sociological Model of Addiction

Beliefs in the Sociological Model of Addiction Moderating the Effect Between Vignette Condition and Public Stigma

Addiction

Mental Health


Medical


Appendix K

Table 4.

Simple Slope Test Comparing the Differences in the Slope Conditions with the Psychological

Model of Addiction as a Moderator of the Relationship Between Vignette Condition and Public

Stigma.

Conditions being Compared β SE β F p

ADD & MH .107 .087 1.51 .219

ADD & ADD/MH .191 .089 4.61 .032

ADD & M .267 .087 9.47 .002

MH & ADD/MH .084 .089 .892 .345

MH & M .160 .087 3.40 .065

ADD/MH & M .076 .089 .733 .392

Note. R2 = .48. ADD = Addiction Condition, MH = Mental Health Condition, ADD/MH = Co-

Occurring Addiction and Mental Health Condition, M = Non-Psychiatric Medical Condition.


Appendix L

Table 5.

Simple Slope Test Comparing the Differences in the Slope Conditions with the Nature Model of

Addiction as a Moderator of the Relationship Between Vignette Condition and Public Stigma.


ADD & MH -.192 .077 6.29 .012

ADD & ADD/MH -.039 .083 .217 .642

ADD & M -.204 .081 6.39 .012

MH & ADD/MH .153 .086 3.19 .075

MH & M -.012 .084 .021 .885

ADD/MH & M -.166 .090 3.40 .065




Appendix M

Table 6.

Simple Slope Test Comparing the Differences in the Slope Conditions with the Sociological

Model of Addiction as a Moderator of the Relationship Between Vignette Condition and Public

Stigma.


ADD & MH .105 .164 .414 .520

ADD & ADD/MH -.329 .161 4.18 .041

ADD & M .014 .159 .008 .928

MH & ADD/MH -.435 .165 6.92 .009

MH & M -.091 .163 .311 .577

ADD/MH & M .344 .160 4.59 .032




Appendix N

Table 7.

Multiple Regression Analyzing whether Individual’s Beliefs in the Five Models of Addiction

Predict Public Stigma for Participants who saw the Addiction and Co-Occurring Addiction and

Mental Health Condition Vignettes.

Beliefs Standardized Coefficients β t Sig. Lower Bound

95% CI for β Upper Bound 95% CI for β

Psychological -.147 -3.408 .001 -.232 -.062

Moral .286 15.117 .000 .249 .324

Nature -.097 -2.479 .014 -.174 -.020

Sociological .121 1.554 .121 -.032 .275

Disease .064 .864 .388 -.082 .210

Note: R2= .40.


Appendix O

Table 8.

Univariate Between-Subjects ANOVA Analyzing Mean Differences in the Acceptance of the

BDMA given Participants Geographical Region.



(Intercept) 207029.28 1 207029.28 5805.85 .000 .870

Country 450.100 2 225.05 6.31 .002 .014

Error 30987.45 869 35.66


Appendix P

Table 9.


95% CI

Country Other Countries


Bound Upper Bound

CAN US -1.52* .454 .002 -2.58 -.45 AUS -.03 .926 .999 -2.20 2.15

US CAN 1.52* .454 .002 .45 2.58 AUS 1.49 .881 .207 -.57 3.56

AUS CAN .03 .926 .999 -2.15 2.20 US -1.49 .881 .207 -3.56 .57

Note: CAN = Canada; US = United States; AUS = Australia


Appendix Q

Table 10.

Post-Hoc Univariate Between-Subjects ANOVA Analyzing Mean Differences in the Acceptance

of the Moral Model of Addiction given Participants Geographical Region.



(Intercept) 920972.847 1 920972.874 1909.281 .000 .870

Country 10099.072 2 5049.536 10.468 .000 .024

Error 419176.305 869 482.366

Note: Bonferroni adjustment (p = .05/5 = .01) was utilized in order to reduce family-wise error

rates as this analysis was conducted and consequently was not a priori.


Appendix R

Table 11.


95% CI

Country Other Countries


Bound Upper Bound

CAN US -7.59* 1.669 .000 -11.51 -3.67 AUS -6.92 3.405 .105 -14.91 1.07

US CAN -7.59* 1.669 .000 3.67 11.51 AUS .67 3.238 .977 -6.93 8.27

AUS CAN 6.92 3.405 .105 -1.07 14.91 US -.67 3.238 .977 -8.27 6.93

Note: CAN = Canada; US = United States; AUS = Australia


Appendix S

Figure 4.

The Moral Model of Addiction Having a Significant Indirect Effect on the Relationship between

Geographical Region and Public Stigma.

Moral Model of Addiction

Geographical Region Public Stigma

b = -3.675, p = .003 b = .107, p = .859

Direct Effect: b = .107, p = .859

Indirect Effect: b = -1.089, 95% BCa CI [-1.764, -.438]

Note: United States was coded as -1 and Canada was coded as +1. Australia was excluded from analyses

Documents

Running head: THE BRAIN DISEASE MODEL OF ......THE BRAIN DISEASE MODEL OF ADDICTION 4 addiction were present, four of which are the same models as Mosher and Akins (2007), with the