Cite as: Humphrey, N. & Wigelsworth, M. (2016). Making the case for universal school-based

mental health screening. Emotional and Behavioural Difficulties [invited article], 21, 22-42.

Making the case for universal school-based mental health screening

Neil Humphrey and Michael Wigelsworth

Manchester Institute of Education

School of Environment, Education and Development

Ellen Wilkinson Building

Oxford Road

University of Manchester

M13 9PL

neil.humphrey@manchester.ac.uk

0161 275 3404

1

Abstract

Mental health difficulties affect 1 in 10 children and adolescents, and up to half of

adult cases begin during the school years.  The individual and societal impacts of

such difficulties are huge, and include poorer quality of life, lost economic

productivity, destabilisation of communities, and high rates of health, education and

social care service utilisation.  Using early intervention and prevention in schools as a

central component of a co-ordinated response to this emergent public health crisis

makes good sense. Schools play a central role in the lives of children and their

families, and their reach is unparalleled.  It has been argued that truly comprehensive

and effective mental health promotion in schools requires a universal screening

component, but this is a controversial proposition.  In this article we explore some of

the opportunities and challenges posed by such a system.  In doing so we critically

assess international literature on social validity (e.g. acceptability, feasibility and

utility), definition and conceptualisation (e.g. what do we mean by ‘mental health’ and

related terms?), design and implementation (e.g. planning, tool selection, linking to

referral and intervention systems), psychometric considerations (e.g. are available

instruments reliable and valid?), diversity (e.g. taking into account cultural variation)

and costs and benefits (e.g. are the human, financial and material costs of universal

screening justified by the improvements in provision and outcomes they bring?).  We

conclude by presenting a vision for a school-based system that takes into account

these important factors.

Keywords: mental health, universal, screening, assessment, schools

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Making the case for universal school-based mental health screening

Introduction

Mental health difficulties (MHDs) are changes in thinking, mood and/or behaviour that impair

functioning (Murphey, Barry, & Vaughn, 2013). In the two major classification systems used in the

field (International Classification of Diseases and the Diagnostic and Statistical Manual) a

distinction is drawn between internalising (e.g. anxiety and mood disorders) and externalising (e.g.

conduct and hyperkinetic disorders) problems (Tyrer, 2014). 1 in 10 children and young people

experience clinically significant MHDs (Green, McGinnity, Meltzer, Ford, & Goodman, 2005), and

50% of adult cases originate in childhood or adolescence (Belfer, 2008). Children who experience

such difficulties are less likely to attend and achieve their potential in school (Colman et al., 2009)

and more likely to be unemployed as adults (Farrington, Healey, & Knapp, 2004). Over the life

course, the individual and societal impacts of MHDs are huge, and can include reduced quality of

life, lost economic productivity, destabilization of communities, and high rates of health, education

and social care utilization (Belfer, 2008). In financial terms, the annual cost is around £105 billion

annually in England (Centre for Mental Health, 2010), of which more than £20 billion is spent on

health and social care (more than double the annual cost of cancer diagnosis and treatment -

Williams, 2013). By 2030, depression alone will yield the highest disease burden in high-income

countries, accounting for nearly 10% of disability-adjusted-life-years (Mathers & Loncar, 2006). It

is our contention that this is an emerging public health crisis. Although the prevalence of MHDs in

childhood and adolescence stabilised in the early years of the new millennium following a sharp

rise in previous decades (Maughan, Collishaw, Meltzer, & Goodman, 2008), spending reductions

child and adolescent mental health services (CAMHS) in the last five years (Buchanan, 2015)

mean that the high proportion of unmet need discussed later in this article will continue to rise as

eligibility/referral thresholds for service access inevitably increase.

School is an ideal setting in which to identify those at risk of developing MHD and intervene early

to address problems before they become deeply entrenched. The nature of schooling provides a

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critical opportunity to effect positive change – it is universal, begins early in life and entails periods

of prolonged engagement with children and young people (totaling around 15,000 hours - Rutter,

Maughan, Mortimore, Ouston, & Smith, 1979) during which effective intervention strategies can be

implemented. In most education systems, a tiered approach to intervention is evident (Weare &

Nind, 2011). Universal mental health provision is designed to reach all children and prevent

problems before they occur by equipping children with the intra- (e.g. self-regulation) and inter-

personal (e.g. empathy) skills that can help them to be more resilient to the onset of MHD during

difficult periods in their lives. An example of this kind of provision is the Second Step social and

emotional learning curriculum (Committee for Children, 2011). For children with nascent MHD,

additional support may be required through selective/targeted interventions that seek to prevent

the progression of symptoms. At this level of provision, interventions start to become differentiated

according to the nature of the difficulties experienced by children. The small group work

component of the social and emotional aspects of learning (SEAL) programme (Department for

Education and Skills, 2006) provides a useful illustration. Finally, indicated interventions are

designed for children identified as having prodromal or established MHD. At this level of provision,

interventions are typically more intensive, lengthy, and often involve health, social or specialist

community services (Shucksmith, 2007). A school-based example can be seen in Bloomquist,

August, and Ostrander's (1991) cognitive-behavioural intervention for children with ADHD.

Numerous systematic reviews and meta-analyses attest to the fact that high quality, well

implemented school-based mental health interventions can effect meaningful change for children

and young people (e.g. Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011; Shucksmith,

2007; Sklad, Diekstra, De Ritter, Ben, & Gravesteijn, 2012; Weare & Nind, 2011; Wilson & Lipsey,

2007). There is also broad agreement that an integrated approach to provision that offers a

synthesis of universal, targeted and indicated interventions is likely to be the most effective model

for schools, offering as it does increased intervention exposure for those who need it most, the

possibility of additive or multiplicative effects brought about through the interaction of different

interventions, improved sustainability, and increased implementation quality (Domitrovich et al.,

2010).

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The case for universal mental health screening in schools

In spite of the substantial and growing evidence base for mental health provision in schools, there

is still significant room for improvement in the system. For example, the majority of schools in

England report using approaches to intervention that have no evidential base (Vostanis,

Humphrey, Fitzgerald, Wolpert, & Deighton, 2013). Additionally, despite the rhetoric of the

importance attributed to mental health, education policy has increasingly encouraged schools to

maximise students’ academic attainment at the expense of their broader wellbeing and health

(Bonell et al., 2014), an issue to which we return later in this article. However, unmet need is

perhaps the most significant of the problems to be addressed. Kelvin (2014) reports that up to

75% of children in the UK who experience significant MHD do not access the support that they

need, whether in the context of education, health or social care services. A similar proportion of

unmet need is evident in other countries such as the USA (Dvorsky, Girio-Herrera, & Owens,

2014).

While this cannot solely be attributed to lack of effective early identification (for example, referral

thresholds to CAMHS are increasingly stringent, and there are inequalities in service access

among certain marginalized groups, such as those in black and minority ethnic communities), it is

undoubtedly a major contributory factor, and on this basis we argue that a critical step-change in

methods and practices is required. Indeed, this is one of the key tenets of the Department of

Health’s recently published Future in Mind strategy document (Department of Health, 2015), which

argues for improvements in, “early identification of need, so that children and young people are

supported as soon as problems arise to prevent more serious problems developing” (p.33).

Despite some recent advances (for example, the 2014 launch of Mind Ed, a Department of Health

web portal designed to help adults working with children and young people spot the early signs of

MHD), we still arguably operate according to two inefficient and ineffective models of service

delivery. The first, ‘refer-test-place’ (Dowdy, Ritchey, & Kamphaus, 2010), involves children with

MHD being referred to a given professional (for example, an educational psychologist or mental

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health worker), who assesses their needs and provides advice regarding appropriate

targeted/indicated provision. The second, what Glover and Albers (2007) call the ‘wait to fail’

model, involves children and young people’s MHD coming to the attention of education, care

and/or health service professionals as the result of events that reflect deeply entrenched problems

(for example, being permanently excluded from school or coming into contact with the criminal

justice system). Both models are fundamentally flawed because they are highly variable and result

in under-referral (thus undoubtedly contributing to the unmet need statistics noted above) and late-

referral (meaning that critical opportunities for early intervention have been missed) (Dvorsky et al.,

2014). Furthermore, they operate against a backdrop of major cuts to child mental health services

in two-thirds of Local Authorities since 2010 (Young Minds, 2013), which have had the effect of

increasing pressure on schools to ‘pick up the pieces’ (O’Hara, 2014). At a broader level, the

current state of affairs in children’s mental health can be seen as a reflective of the problems

associated with societal inequality. Wilkinson and Pickett (2010) highlight international evidence of

the inverse relationship between child wellbeing and income inequality. The UK provides a useful

case in point – extremely high levels of income inequality, juxtaposed against child wellbeing levels

that were the lowest recorded among 21 developed counties in 2007 (UNICEF, 2007)1.

So, what is to be done? It has been argued that a critical prerequisite to providing effective school-

based prevention and intervention services is the adoption of a population-based approach

embodied by a universal screening model. In such a system, all members of the student population

in a school undergo brief assessments (which may be informed by teachers, parents and/or

students themselves) designed to identify those at risk of developing MHD (Dowdy et al., 2010;

Dvorsky et al., 2014; Glover & Albers, 2007). The logic is simple: “before intervention can occur

mental health problems must be identified” (Williams, 2013, p.5478), and periodic universal

screening beginning as early as possible means that MHD can be identified before they reach

clinically significant levels. Dvorsky et al (2014) propose three key benefits of such a system.

First, by definition, universal screening means that all children and young people are assessed.

Theoretically, this should have the effect of reducing the number of those at-risk being overlooked

1 This ranking has since improved and the UK currently stands in 16 th place (UNICEF, 2013), but this is mitigated by the fact that five of the lower ranking countries are new entries and amongst the poorest in the survey (e.g. Romania).

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compared to the existing methods noted above. Second, universal screening provides a baseline

for future monitoring and assessment. This means that a more data-driven approach to mental

health provision in schools can be adopted. Third, universal screening can offer significant cost-

savings over time. The basic logic here is that universal screening should lead to earlier

intervention for emergent MHD, which is less intensive and expensive than indicated interventions

for more severe or entrenched problems. However, despite these apparent benefits, universal

mental health screening is extremely rare. For example, only 2% of schools in the USA use this

approach as part of their routine practice (Romer & McIntosh, 2005). This contrasts sharply with

screening for physical health indicators (e.g. vision, hearing), which have been universally

assessed for decades (Dowdy et al., 2010; Williams, 2013). What accounts for this discrepancy? It

may reflect the fact that mental health has traditionally not been given equal weight to physical

health in public policy (H. M. Government, 2010). The stigma associated with mental health is also

a likely contributory factor (Dowdy et al., 2010; Evans-Lacko et al., 2014). In truth, there are

multiple challenges that mean universal school-based screening for MHD is ‘easier said than

done’, and through the course of this article we discuss these in addition to the opportunities it can

provide.

Social validity

Social validity refers to the value and social importance attributed to a given innovation by those

who are direct or indirect consumers of it (Hurley, 2012; Luiselli & Reed, 2011). In this case, they

are teachers and other school staff, parents, pupils and external education, health and social care

professionals. Social validity is a critical but often overlooked consideration when new initiatives

are launched in education. Adapting Wolf's (1978) classic taxonomy, it can be thought of in terms

of acceptability (e.g. are the intended outcomes of an innovation wanted, needed, and/or socially

significant?), feasibility (e.g. are the procedures employed to achieve the intended outcomes of the

innovation considered to be acceptable?) and utility (e.g. are the outcomes of the innovation

considered to satisfactory?). We begin our discussion of universal school-based screening for

MHD on this issue because it is so fundamental. Without demonstrable social validity, innovations

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in education or elsewhere are unlikely to be widely adopted. Currently, “there is a dearth of

information regarding the acceptability of screeners and screening process, along with the social

importance of the effects of screening” (Dvorsky et al., 2014, p.307). However, that which is

available provides extremely useful insights.

In terms of acceptability, there is little doubt that the goal of preventing MHD and promoting

wellbeing among children and young people is considered to be socially important among the

various stakeholders noted above. However, there are some concerns about the concept of

universal screening as a means through which to achieve this. Among these, the idea that it will

be a stigmatising process is common (Williams, 2013). At first, this may seem like an oxymoron –

surely a universal approach would be destigmatising as no single person or group are singled out?

However, concerns remain about the problem-focused nature of assessments (see later section on

conceptualisation of mental health) and the possible later consequences in terms of ‘typing’ (e.g.

the screening results trigger the application of a prototype for MHD that then acts as a filter through

which the child’s future behaviour is observed). This is a particular issue in ‘false positive’ cases,

where a screening instrument categorises a child incorrectly as at-risk. Sensitive handling of

screening, identification, referral and intervention processes is therefore fundamental, alongside

explicit efforts to reduce stigma that may include provision of mental health literacy initiatives for

staff (e.g. Kutcher, Wei, McLuckie, & Bullock, 2013) and students (e.g. Wei, Hayden, Kutcher,

Zygmunt, & McGrath, 2013).

There are also criticisms (albeit primarily among academic activists rather than the stakeholders

noted above) of the notion that children’s wellbeing could/should become the focus of checklists

(e.g. Watson, Emery, & Bayliss, 2012) in an education system already driven by ‘testocracy’2.

Finally, there are ethical questions, ranging from basic notions of informed consent (e.g. consent

may be the most difficult to obtain from parents/carers of children most at risk for developing MHD

- Levitt, Saka, Hunter Romanelli, & Hoagwood, 2007) to more complex questions about the

2 Testocracy refers to the domination of standardized, quantifiable merit in education – for examples, the prominence of school league tables and attainment targets.

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purpose, ownership, use and sharing of the data created through universal screening, including

issues of community acceptance and family rights (Chafouleas, Kilgus, & Wallach, 2010).

In relation to feasibility, Connors, Arora, Curtis, and Stephan's (2014) survey of evidence-based

assessment among school mental health clinicians in the USA reported that most common and

widely used mental health measures (for example, the Strengths and Difficulties Questionnaire -

Goodman, 1997) were perceived as being easy to administer. However, respondents in the same

study also raised concerns about difficulties reaching parents and children or parents not

understanding assessment questions. Dvorsky et al., (2014) note further pragmatic considerations

such as brevity, cost and simplicity. Gresham and Elliot’s (2008) ‘Performance Screening Guide’

(PSG) aspect of the Social Skills Improvement System provides a useful example. Teacher

feedback on the piloting of this instrument noted clear instructions and definitions/descriptors of

domains of interest as being vital to instrument’s feasibility as a screener. Lengthy, expensive

measures that have complicated scoring protocols are unlikely to be widely adopted by schools.

There is evidence, however, that these requirements need not outweigh other considerations, such

as psychometric characteristics. For example, Connors et al (2004) and Zuckerbrot et al. (2007)

both highlight a range of robust measures that can be completed in 5 minutes or less.

Finally, with regard to utility, the aforementioned study by Connors et al (2014) found a generally

high level of perceived clinical value for common mental health measures. Similarly, a survey of

over 300 head teachers by Taggart et al. (2014) found that among those whose secondary schools

routinely used some kind of screening measure, 85% reported that they were effective (although it

should be noted that the exact nature of ‘screening’ and what ‘effective’ means was not reported

and is therefore open to interpretation). Gresham & Elliot (2008) also report positive feedback from

teachers regarding the PSG, most notably that the instrument (which covers academic and non-

academic domains, e.g. pro-social behaviour) referred to useful behaviours and helped to provide

a more comprehensive assessment of their students. Finally, the aforementioned study by

Zuckerbrot et al (2007), which focused on the pilot of a screening tool for adolescent depression in

primary care, reported that most providers in their sample felt that it had aided their identification of

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children in need of help, made them feel more comfortable in addressing depression, and that they

would continue using the tool.

Education policy and the ‘zero sum game’ approach to attainment and health

Irrespective of the evidence that universal screening (and, by extension, mental health promotion

more generally) is considered to be a socially important innovation by key stakeholders such as

teachers and school leaders, it is vital to also consider the policy directives and mandates under

which they operate, since it is often these that dictate whether such innovations gain traction ‘at the

chalkface’. In this sense, the current education policy landscape presents a major challenge.

Since 2010, successive governments (first Coalition, subsequently Conservative) have actively

dismantled key aspects of the education system that supported an emphasis on pupil health and

wellbeing in schools. To whit, government endorsement of the social and emotional aspects of

learning programme was withdrawn; participation in the National Healthy Schools programme is no

longer funded; personal, social and health education remains non-statutory; the Ofsted inspection

framework no longer focuses explicitly on personal development and wellbeing; and an initiative

that effectively replaces the early years foundation stage profiling exercise with baseline academic

testing on school entry is currently being trialled with a view to a national scale-up in 2016. These

policy shifts are part of a highly rationalist/technicist model of education, and reflect what Bonell et

al (2014) have termed the ‘zero-sum game’ view of attainment and health (e.g. academic

attainment is singularly important in promoting economic competetiveness; any time spent on

improving health and wellbeing in schools means less time for traditional academic instruction and

thus produces lower attainment).

Schools are therefore likely to need a signficant ‘hook’ and it is here where the growing body of

evidence on the relatonship between mental health and academic attainment comes to the fore. In

particular, research on developmental cascades has shown how, for example, early mental health

difficulties can erode later academic attainment (e.g. Moilanen, Shaw, & Maxwell, 2010). Similarly,

research is available that challenges the view of academic attainment as the sole driver of

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economic competitiveness (e.g. Goodman, Joshi, Nasim, & Tyler, 2015). Active promotion and

dissemination of such evidence is likely to be a necessary part of any campaign to introduce

universal screening in schools given the current education policy landscape.

Definition and conceptualisation

What we mean and understand by ‘mental health’ and related terms (e.g. social and emotional

wellbeing) is obviously a critical consideration for universal screening. The discourse of mental

health is extremely controversial, and there is a long-standing objection to the ‘illness framework’

(e.g. disease and disorder) (Pilgrim, 2014; Rogers & Pilgrim, 2014) particularly as applied in the

educational context (Graham, Phelps, Maddison, & Fitzgerald, 2011). This conceptualisation can

be seen as negative, stigmatising, and problematizing those experiencing difficulties, while also

serving to reinforce social exclusion of marginalised individuals and groups (Link & Phelan, 2006).

As a result, ‘difficulties’ or ‘problems’ have begun to replace ‘disorders’ in the collective mental

health lexicon as these terms are generally considered to be less value-laden (Pilgrim, 2014), but

even this may be viewed as problematic. Consider this article. Thus far we have referred to

mental health difficulties, and discussed the identification/classification of children and young

people who may be at-risk of developing internalising and/or externalising difficulties, symptoms

and/or problems. To some this kind of language will be considered retrograde.

Partly in response to the issues noted above, recent years have seen the emergence of an

‘enhancement agenda’ embodied by a strengths-based discourse that emphasizes agency and

resilience (Graham et al., 2011). Viewed through this lens, mental health is fundamentally about a

state of wellbeing as opposed to illness, and shifts in thinking about the role of schools have

accompanied this. Thus, we have seen a dramatic increase in the popularity of universal social

and emotional learning programmes and other approaches that focus on the promotion of

competencies rather than the remediation of problems (Humphrey, 2013; Vostanis et al., 2013).

More broadly, wellbeing and in particular ‘the pursuit of happiness’ (Centre Forum Commission,

2014) have entered the mainstream discourse of mental health, and the positive psychology

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movement has gained a significant foothold in education (Furlong, Gilman, & Huebner, 2014).

However, this model also has its detractors. For example, Ecclestone and Hayes (2008, 2009)

and others (e.g. Watson, Emery, and Bayliss, 2012) have critiqued the increased attention on the

promotion of wellbeing in education, arguing that in spite of its apparently positive focus, this

approach is still fundamentally about vulnerability and the diminishment of the human subject. One

might also argue that an approach to understanding mental health that purposively ignores or

underplays notions of distress risks drawing much-needed support away from those who need it

most.

Somewhere in between is the ‘dual factor’ approach, which conceptualises mental health as

comprising two distinct dimensions, representing the experience of symptoms of psychological

distress and adaptive functioning respectively (Dowdy, Kamphaus, Twyford, & Dever, 2014).

Importantly, this model posits mental illness and health not as forming a single continuum, but

rather as complementary but discrete continua (Dowdy et al., 2014). This model of mental health

is illustrated in NICE’s current definition, which posits emotional, social and psychological wellbeing

as each including these distinct dimensions (e.g. emotional wellbeing is viewed in terms of

happiness and confidence and anxiety and depression) (National Institute for Health and Care

Excellence, 2013).

Even with such a framework, it is important not to underestimate the influence of different

professional lexicons and the barriers these may produce. Indeed, mental health can be seen as a

microcosm of the wider challenges associated with multi-agency collaboration in children’s

services more generally. The lack of a common language is a central component of this (Richford,

2013), and there will likely be differential reactions to a screening instrument that purports to focus

upon (for example) ‘mental health (difficulties)’, ‘social and emotional wellbeing’, or ‘emotional and

behavioural difficulties’, among education, health and social care professionals. This is important

because the terminology used about and within a screening system will influence its perceived

acceptability among those who make use of it. For example, it may be that a focus on children’s

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‘social and emotional wellbeing’ may be considered more palatable among teachers than one that

is seen as foregrounding ‘mental health difficulties’.

In terms of content, a screener that mirrors the dual factor model outlined above perhaps offers the

most acceptable and comprehensive approach (Dowdy et al., 2014). However, the inclusion of

domains relating to children’s social-emotional competence, resilience and/or adaptive functioning

more broadly can only be useful if the data produced is subsequently used to inform provision in a

manner that is consistent with the underlying model. So for example, areas of strength could be

highlighted in order to prompt discussion about how these may be capitalised upon in order to

mitigate the impact of difficulties being experienced in other domains. Alternatively, dual factor

data could be used as a means to identify students at the very highest levels of risk (e.g. high

symptoms/distress, minimal competence/strengths) who may be require immediate and intensive

intervention (Dowdy et al., 2014). Unfortunately, this does not seem to be a feature in current

screening practices. Where screening instruments do include domains relating to areas of

competence or strength (e.g. Skaar, Christ, & Jacobucci's (2014) Pro-social and Health Adolescent

Risk Behaviour Scale (PHARBS) incorporates a pro-social behaviour domain), these are often

simply reverse-engineered during scoring to fit a deficit model (and thus, the PHARBS pro-social

subscale is used to identify pro-social risk).

Design and implementation

Training and goal clarification

Initial training and goal clarification for school staff are critical for a number of reasons. From a

technical standpoint, training can help to improve: (i) awareness of mental health and MHD; (ii)

consistency of screening across informants; (iii) understanding of how to score and use the data

generated through screening at different levels (e.g. individual, class, school); (iv) communication

with parents; and (v) referral and intervention practices following screening (Dvorsky et al., 2014).

From a conceptual standpoint, training may also be used as an opportunity to clarify the intended

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purpose of screening and the role it plays in the wider system of mental health provision in a given

school. This, alongside alleviation of pragmatic concerns likely to be expressed by many teachers

(e.g. the time taken to engage in the screening process, how the data will actually be used) will

likely increase the perceived social validity of the system (and thus increase the likelihood that it

will be adopted, implemented with consistency and sustained over time).

Measure selection and approach to screening

Assessment of child and adolescent mental health is a burgeoning field, and there certainly is no

shortage of available measures that could theoretically be used in a universal screening system.

For example, a recent systematic review by Deighton et al., (2014) identified 117 measures,

filtering down eventually to 11 ‘best of field’ tools (e.g. those with the strongest evidence base).

Similarly, a systematic review of measures of children’s social-emotional competence by our

research team identified 189 before subsequently filtering down to 12 (Humphrey et al., 2011;

Wigelsworth, Humphrey, Kalambouka, & Lendrum, 2010). Available instruments vary greatly in

what they purport to measure (e.g. mental health difficulties, social-emotional competence), their

scope and specificity (e.g. some provide a broadband index of MHD, whereas others focus on a

specific aspect, such as depression), implementation characteristics (e.g. age range, informant

type, length and completion time) and psychometric properties (see section on this below). Some

measures (e.g. the Beck Youth Inventory - Beck, Beck, & Jolly, 2001) are proprietary and there are

therefore financial costs associated with their use; others (e.g. the Strengths and Difficulties

Questionnaire - Goodman, 1997) are freely available for widespread use. Instrument selection

should take into account these factors, while also balancing the goodness of fit with local need

(e.g. intended purpose, developmental appropriateness, required timing and frequency of

assessment) (Dvorsky et al., 2014; Glover & Albers, 2007).

However, it is also important to note that standardised measures of mental health are not the only

means through which to operate a universal screening system. Other approaches include

screening for exposure to risk factors associated with MHD (e.g. exposure to domestic violence),

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or using simple nomination methods (e.g. asking teachers to nominate children they consider to be

at increased risk of developing MHD). Dwyer, Nicholson, and Battistutta (2006) report on the utility

of both methods, and we explore this study in the section on psychometric considerations. Of

particular note at this juncture is their overall conclusion that, “Simple nomination deserves

attention as a screening method… the results of the current study suggest that simple nomination

may be used to identify children for selective interventions with reasonable confidence that its

predictive validity is as good, if not better than, other currently available (longer) screening

instruments” (p.352).

Thus, nomination may offer an effective and expedient alternative to standardized measures. That

said, we should also consider the findings of Miller et al. (2014), whose study found that

standardised measures were more accurate and reliable than school nomination. However, it is

important to note that the manner in which the latter study conceptualized ‘nomination’ was very

different to the Dwyer study, being based upon evidence of referral and supplementary support for

a student rather than a teacher’s perceptions of their likelihood of developing MHD.

In some studies of universal screening, the above methods are combined in a multi-stage system.

For example, in Kettler, Elliott, Davies, and Griffin's (2012) study, ratings on the aforementioned

PSG were used as a gateway to further assessment using the more detailed Social Skills

Improvement System measure (Gresham & Elliot, 2008). The authors’ analyses indicated that,

“these types of tools appear to work well in multiple settings for assigning students to relatively

inexpensive non-restrictive interventions, or as first stages in multi-stage systems for screening”

(Kettler et al, 2012, p.108). The initial nomination stage was highly sensitive, correctly identifying

almost all students in need, but also misidentifying many who did not need help. While this

misclassification is of concern given the possible consequences of ‘false positive’ identification

noted earlier, it does suggest that nomination may be an effective (and extremely time efficient)

first stage in a universal screening model. In such a model, one of the purposes of a more detailed

assessment at the second stage could conceivably be to ‘separate the wheat from the chaff’ vis-à-

vis true positives and false positives identified through nomination before referrals for intervention

15

are made. Other models exist that entail additional layers of assessment prior to referral that may

increase the accuracy of identification practices even further (see for example the Systematic

Screening for Behavior Disorders approach outlined by Severson, Walker, Hope-Doolittle,

Kratochwill, and Gresham, 2007), but these may become impractical because of the additional

data requirements with each successive stage.

Informants

A fundamental consideration in the design of universal screening is who actually provides

information (e.g., teacher-, parent- and/or self-report) (Dvorsky et al., 2014). Use of child self-report

aligns with calls for an increased focus on the child’s perspective (e.g. Children Act - H. M.

Government, 2004) and also offers a feasible model for a population-based approach when

compared to the possible data burden imposed by using teacher-report. There is evidence that

children as young as 7-8 are able reporters of their mental health, particularly in relation to

internalizing difficulties (e.g. Sharp, Goodyer, & Croudace, 2006). However, below this age the

information provided may be questionable because of the literacy, self-awareness and competence

in social understanding required to complete screening measures (that is, younger children may

provide unreliable data because they cannot access the written text properly, do not yet have the

level of self-insight required, and/or are not yet able to use social comparison as an appropriate

frame of reference for understanding their thoughts, feelings and behaviour). Children generally

report higher levels of difficulties than their parents, but report less impact of these perceived

difficulties (Van Roy, Groholt, Heyerdahl, & Clench-Aas, 2010).

Teachers are a viable alternative and are often the primary informant in school-based mental

health screening (Dowdy et al., 2010). They benefit from seeing a child’s behaviour in school and

can also use their collective experience with other children as a frame of reference in providing

accurate ratings. However, the burden imposed and loss of sensitivity in the measure itself when a

single teacher is required to complete it about every child in his/her class (as is common practice in

universal screeners) represent substantive limitations of this approach. There are also concerns

16

that teachers are less accurate in identifying internalizing difficulties than externalizing problems

(the latter being more salient to classroom management) (Atzaba-Poria, Pike, & Barrett, 2004), and

indeed they report feeling less equipped to understand the signs of emotional distress in children

(Papandrea & Winefield, 2011). These issues notwithstanding, teacher ratings typically have

greater predictive validity than other informants, although this may be at least in part attributable to

shared rater variance (e.g. teachers have more influence on some of the outcomes being predicted

- such as academic grades - than parents; Dowdy et al., 2010).

Parents, by contrast, can provide detailed information about children’s behaviour in the home

context, but typically have quite a restricted frame of reference when compared to teachers

(Wigelsworth et al., 2010). Like teachers, they are generally considered to be accurate raters of

externalizing problems but may significantly under-estimate internalizing difficulties (Constant et

al., 2014; Dwyer et al., 2006). As noted earlier, there are also legitimate concerns about reach and

participation in relation to parents of at-risk children (Connors et al., 2014). Given this, it should

come as no surprise that a comprehensive, multi-informant approach is recommended. Each

informant provides unique information that is superior to that provided by the others alone.

Furthermore, a multi-informant approach may yield concurrent advantages in terms of better data

monitoring and increased communication between teachers and parents (Dowdy et al., 2010;

Dvorsky et al., 2014). As is seen below, this proposition is also supported by psychometric

evidence. Nonetheless, concerns remain about the feasibility of such a system, which could easily

become unwieldy and impractical.

Use and sharing of data: the question of ‘what happens next’

Irrespective of other considerations discussed in this paper, universal screening in schools can

only meet its ultimate aim of improving mental health and wellbeing and reducing the distress

experienced by children and young people if the data it produces is used effectively and efficiently.

So, for example, it is vital that screening is properly connected to follow-up services (e.g.

monitoring and/or further assessment, early intervention) both in and out of school - what Dvorsky

17

et al. (2014) call the “coordination of care” (p.305). An important issue to be addressed here is the

notion of ‘treatment validity’, e.g. the extent to which a given assessment procedure contributes to

improved outcomes by clearly signposting areas of concern that can be explicitly linked to

particular forms of recommended intervention (Cook, Volpe, & Livanis, 2010). At this point, it is

worth returning to the research cited earlier that shows that the majority of schools in England

report engaging in interventions that have no evidential base, being mainly locally developed and

untested (Vostanis et al, 2013). The same study reported that almost all schools rely on their staff

to lead interventions, but that few consider staff training, consultation, and/or supervision as a key

part of their overall approach to supporting children’s mental health. This is a salient reminder that

universal screening has to be seen in the wider context of the need for reform in other aspects of

school mental health provision. As noted in the introduction to this article, there is no shortage of

evidence that high quality, well implemented school-based mental health interventions can effect

meaningful change for children and young people, so perhaps what we need know more about is

how to by improve the ‘science to service’ link and thus increase the scale-up and sustainability of

those which are more likely to produce better outcomes (August, Gewirtz, & Realmuto, 2010).

Alongside this, questions are raised regarding training, expertise and capacity in schools, and of

course funding, to which we return in the section on costs and benefits.

Sharing of data with parents and other stakeholders is a key component of the linkage process,

particularly when the involvement of agencies external to the school (e.g. educational psychology

and/or child and adolescent mental health services) is likely to be necessary, as in cases where an

indicated intervention appears to be warranted. What data is shared with parents and how it is

presented appears to be absolutely vital, as this may influence not just awareness but help seeking

(e.g. service utilisation) and other changes in behaviour (Dvorsky et al., 2014).

There is also considerable potential for other uses of the data generated through universal mental

health screening in schools. Aggregation of data to class, year and/or school levels can provide

important indicators of the wellbeing of the school population and subgroups within it (e.g. those

with special educational needs, those eligible for free school meals). If the frequency and timing of

18

administration permits, this data may also be used to monitor and evaluate universal provision in

the school or be used in school improvement planning more generally. These uses may be

particularly salient given the increasing requirements for schools to provide ‘hard’ (e.g. quantifiable)

evidence of pupil outcomes in areas of functioning where routinely collected data (e.g. attendance,

attainment, exclusions) may not be appropriate or sufficient. So, for example, data collected

though universal screening could feasibly be used to provide evidence for the schools

inspectorate, Ofsted. An obvious candidate for data linkage from universal screening is the

Behaviour and Safety strand of the inspection framework. Inspectors are also required to consider

the spiritual, moral, social and cultural development of pupils, while also considering the extent to

which the needs of a range of pupils (including those with special educational needs) are met

(Office for Standards in Education, 2012). Of note is the requirement that, “judgements must not

be made solely on the basis of what is seen during an inspection. Inspectors must take into

account a range of evidence… over an extended period… [including] the results of any surveys

carried out or commissioned by the school” (ibid, p.18-21). Hence, universal screening may yield

particularly strong ‘added value’ above and beyond early identification of MHD.

Psychometric considerations

The technical adequacy of a given screening instrument is of course vital if it is to serve its

intended purpose. Drawing upon Terwee et al's (2007) proposed quality criteria for health status

questionnaires, systematic reviews of measures in child mental health and related fields (e.g.

Deighton et al., 2014; Humphrey et al., 2011) and Glover and Albers' (2007) suggested

considerations for universal screening assessments, we propose the following validation criteria: (i)

content validity (are the constructs of interest comprehensively represented by the items in the

measure?) is evident where the instrument has a clear measurement aim, target population,

description of measurement domains, has been through a rigorous item selection and reduction

process, and contains items with a high degree of interpretability (e.g. low reading level, short,

simple items); (ii) internal consistency (are items in a (sub) scale homogenous and thus measuring

the same construct?) can be demonstrated through exploratory and/or confirmatory factor analysis

19

that establish the presence of a single or multiple domains, followed by Cronbach’s Alpha co-

efficients for each (sub)scale in the 0.70-0.9 range; (iii) criterion validity (do scores on a given

instrument relate to a gold standard measure?) is considered strong with a correlation of 0.70 or

greater with an existing, well-validated measure; (iv) construct validity (do scores on a given

measure relate to other outcomes in a way that is theoretically consistent with the constructs being

assessed?) can include, for example, the ability of the screener to distinguish between ‘known’

groups (e.g. with and without referrals to child and adolescent mental health services); (v)

reproducibility (do repeated assessments in stable individuals produce similar results?) can be

assessed by examining levels of agreement or reliability, such as a Kappa co-efficient of at least

0.70; (vi) responsiveness (can the measure detect clinically important change over time?) can be

shown through, for example, the ability of the measure to distinguish between individuals who have

and have not changed (as classified by an external criterion) with a receiver operating

characteristics area under the curve of at least 0.70; and (vii) interpretability (are scores meaningful

in a qualitative sense?) can be aided by the availability of descriptive statistics (e.g. means and

standard deviations) for a representative reference population and any relevant subgroups.

In addition to the above, we propose that viable measures must have demonstrable predictive

validity (do scores accurately predict a future state that is theoretically consistent with what is being

measured?). Although technically a subset of construct validity (see above), predictive validity is

so central to the concept of universal mental health screening that it warrants special

consideration. Glover and Albers (2007, p.120) suggest that it can be thought of in terms of

sensitivity (e.g. of those later found to be at-risk, what proportion is correctly identified?), specificity

(e.g. of those not later found to be at-risk, what proportion is correctly identified?), positive

predictive value (PPV) (e.g. what proportion of all those identified as at-risk were ‘valid positives’?)

and negative predictive value (NPV) (e.g. what proportion of all identified as not being at-risk were

‘valid negatives’?). These indices arguably represent the most important benchmark for the utility

of universal screening measures. However, both the NPV and PPV vary as a function of the base

rates of the construct being assessed (e.g. as the prevalence of MHD in a given population

increases, the PPV increases and the NPV decreases) (Dwyer, Nicholson & Battistuta, 2006), and

20

as such the predictive validity of a given instrument will likely fluctuate depending upon the level of

need at a local level. Also, the veracity of predictive validity rests upon the assumption of a robust

external criterion for the determination of the future state that is assessed (e.g. that ‘measure X’,

used as the indicator of the future state, is not subject to error and can be considered an accurate

index of a child’s mental health).

In a useful illustration of the complexities of deterring the predictive validity of universal mental

health screening, Dwyer, Nicholson, and Battistutta's (2006) aforementioned study assessed the

accuracy of a simple nomination method (e.g. informants are asked whether a given child has, in

their opinion, a higher than average chance of developing MHD) and a family risk-factor checklist

(e.g. exposure to adverse life events, poor parenting practices) as predictors of the presence of

MHD one year later (as determined by scores on the Child Behaviour Checklist - Achenbach,

1991). For all three measures, data was collected from both teachers and parents. The authors’

analyses suggested that the accuracy of both screening models varied as a function of informant

(parent, teacher), domain of MHD (internalizing, externalizing) and presence of symptoms at

baseline (present, absent), from which we can infer that a comprehensive, multi-informant

approach is therefore likely to provide optimal sensitivity and specificity.

Accommodation of diversity

Any universal education initiative rightly needs to be “culturally competent” (Dowdy, Kamphaus,

Twyford, & Dever, 2014, p.311) before it can be considered fit for purpose. By this we mean it

reflects and is respectful of the various forms of diversity (e.g. gender, cultural, socio-economic,

ethnic, linguistic) inherent in the school population. At a practical level, this means that universal

mental health screening instruments need to be as accessible as possible (e.g. making translated

versions available for parents and/or children whose first language is not English, or ensuring that

the reading age of instructions and items is as low as possible to improve accessibility for those

with literacy difficulties). However, there are also concurrent psychometric and technical

considerations. Translated versions need to demonstrate measurement equivalence in order to be

21

valid. Use of differential item functioning analyses during the validation of a given instrument is

recommended here as it is able to inform the selection of items that behave equivalently across a

range of various subgroups of the school population (see for example Deighton et al's (2012)

validation of the Me and My School measure of MHD). An alternative approach is offered by Cook

et al. (2010), who question the idea that a single threshold score will equally and accurately predict

risk status across all children and schools. They suggest that instead, we may conceptualise the

various factors noted above as moderators of screening outcomes that need to be factored into

analysis and scoring alongside pertinent school level contextual variables (e.g. urbanicity, climate),

ultimately producing more dynamic classifications. Although this would require a very large and

diverse normative sample and would be technically complex to operationalize, it raises the

possibility of a screening system that is sensitive to both individual difference and context.

At a broader level, screening models must be respectful of different cultural norms and practices,

particularly where these apply directly to mental health. For example, there is considerable cultural

variation in understandings of the dual concepts of mental health and illness and the extent to

which they are (or should be) discussed openly (Fernando, 2010). Alongside this, issues of

acculturation, discrimination (whether perceived or real) and openness to services are of

paramount importance (Dowdy et al., 2014).

Costs and benefits

For universal school-based screening to have any hope of improving early identification methods

and practices in child and adolescent mental health, the evidence needs to demonstrate that this

model is demonstrably superior to existing provision in a variety of ways. Given that the research

in this area is in its relative infancy, there are currently many more questions than answers. We

still know relatively little about the costs (human, financial, material, other) associated with different

screening models (e.g. single vs. multi-stage, nomination vs. standardized measure). Williams

(2013) provides a basic estimate of £27 per child using the child self-report Beck Youth Inventory

22

as an exemplar screening tool, extrapolating that all 7 year-olds3 in the UK could be screened for

less than £18.5 million. This is weighed against the savings that could be made through earlier

intervention (for example, £115,000-150,000 per case for conduct disorder; Williams, ibid) as a

result of screening. However, this estimate is somewhat rudimentary and makes a number of

assumptions that would influence cost and acceptability (for example, use of a proprietary measure

as opposed to one that is publically available at minimal or no cost - Connors et al., 2014), while

also presuming that screening would result in appropriate referral for early intervention (as yet

unproven – although see below).

In a more rigorous analysis, Kuo and Stoep (2009) provided detailed cost estimates of a universal,

multi-stage screening system for 11-12 year-olds (the ‘Developmental Pathways Screening

Program’ – DPSP). Per student costs4 were determined to range from £4.53 (first-stage, universal

screening) to £24.99 (second-stage, clinical evaluation for those students who screened positive in

the first stage), averaging out at between £5.78 to £8.88 per student within a given school

depending upon the proportion who screen positive at the first stage. These estimates are

promising as they suggest that a robust screening system need not be prohibitively expensive.

Indeed, there are means through which the cost could be reduced even further. For example, the

first stage of the DPSP involves completion of written questionnaires, with associated material (c.

14p per student) and scoring (c.£1 per student) costs (Kuo & Stoep, 2009). We would argue that

the use of a secure online portal would likely be more cost-efficient, even after factoring in the

costs of its development and maintenance.

Alongside estimates of the financial costs associated with the introduction of universal screening,

consideration also needs to be given of how such a system would be funded. If we take Williams’

(2013) proposal as a very high-end estimate (see discussion of this above), the c.£18.5m required

to establish school-based universal screening nationally would represent around 0.09% of the

aforementioned £20 billion annual expenditure on health and social care services associated with

3 7 is the lowest age at which the BYI can be administered.4 All costs converted from USD and correct as at May 2015.

23

MHD (Centre for Mental Health, 2010), or approximately 0.03% of the £54 billion annual budget of

the Education Funding Agency, who support state-supported education for children and young

people up to the age of 19 (see www.gov.uk/government/organisations/education-funding-agency).

Given the government’s recent commitment to improving early identification of need in relation to

MHD (Department of Health, 2015), it is not infeasible to suggest that one or both of these budgets

could and should be used to fund universal screening.

Taking an alternative, more ‘localized’ (as opposed to centralized) estimation, the cost of

implementing universal screening in a single form entry primary school with an average class size

of 27 (Department for Education, 2011) at the beginning of Key Stage 1 and Key Stage 2 would be

£1458, marginally more than the current £1320 annual pupil premium funding allocated for a single

child registered as eligible for free school meals in primary education (www.gov.uk/pupil-premium-

information-for-schools-and-alternative-provision-settings). Of course, there are other costs

associated with the downstream reforms to school-based mental health provision (e.g. training of

staff, provision of effective interventions) needed in order for the potential benefits of universal

screening to be realized, but our interest here is in the discrete costs of the system itself.

What about the short and long-term benefits of universal screening? In relation to the latter, many

questions remain. Does universal screening reduce or prevent the incidence and/or severity of

MHD over time? Does it reduce the proportion of unmet need? Does it reduce the time taken for

children and young people to receive the services they need? The simple answer is that we do not

yet know for sure; these are all critical questions that future research needs to address (Dvorsky et

al., 2014). With regard to the former, however, there is emerging evidence of the superiority of

screening when compared to traditional methods. For example, Eklund and Dowdy's recent study

(2014) demonstrated that universal screening identified a greater proportion of at-risk students

than standard teacher referral practices (akin to the refer-test-place model noted above) across a

sample of nearly 900 primary-aged children. The profile of externalizing difficulties among these

previously ‘unidentified’ children was suggestive of significant unmet need, being two standard

deviations higher than those identified through traditional methods. However, the authors also

24

found a subgroup of students identified through standard school methods that were not picked up

by the screener. Assuming the low likelihood of ‘false positives’ in the refer-test-place model, this

rather telling finding indicates that the sensitivity of universal screening tools likely requires further

optimization, and/or that it should be used in conjunction with existing methods rather than as a

replacement for them.

Finally, there is tentative evidence that universal screening may lead to improved service access,

at least in the short term. The study by Kuo and Stoep (2009) noted earlier reported that at 6-week

follow-up, 72% of referrals (e.g. to a school counselor or outside mental health agency) produced

through the DPSP screener had resulted a successful linkage to a given service/intervention (e.g.

school counsellor). This data needs to be interpreted with caution as we do not know how the

proportion of successful linkages within the time-frame compared to usual practice in the

participating schools – although given the figures noted earlier with regard to unmet need, we can

perhaps assume that this represents a considerable improvement.

Conclusion – a vision for universal school-based mental health screening

We began this article by arguing that there is an emerging public health crisis in relation to MHD

among children and young people. Both the evidence base and basic common sense dictate that

the school system is a crucial component of an effective response to this crisis. The notion of early

intervention and prevention is not novel in this context, but has arguably been granted a new ‘lease

of life’ in the last five years and this is clearly reflected in the current and recent policy discourse

(e.g. Allen, 2011; Department of Health, 2015). Despite this, there has been remarkably little

movement or innovation with regard to the fundamental question of how early identification – which

of course is a necessary prerequisite of early intervention – can be improved. In this paper we

have presented the case for universal, school-based mental health screening as a central

component of service reform in this area. From the preceding discussion of theory and research

on screening it should be clear that we do not view this as some kind of magic bullet. Indeed,

there are still many critical questions that remain to be answered. However, the evidence of

25

potential is very strong, and we therefore propose that universal screening deserves serious

consideration as the central mechanism through which we may make the necessary improvements

to early identification methods and practices in this field. The current context is ripe. Now more

than ever mental health is at the forefront of public policy, and in spite of the aforementioned

issues in relation to education policy post-2010, there have been signs that mandate of

responsibility for pupil mental health and wellbeing in schools (Severson et al., 2007) is beginning

to gain renewed vigour (e.g. Department for Education, 2014).

On the balance of the evidence discussed, what might this look like on the ground? We envisage a

secure online screening system underpinned by high quality training for teachers (and other

stakeholders) that provides a solid baseline of mental health literacy, the technical process of

screening and clarification of its purpose, goals and role within the broader system. The screening

process is undertaken on at least an annual basis in order to provide adequate data for regular

identification of new cases and monitoring of the progress of those who have previously been

identified (thereby allowing for an adaptive system of intervention). In the early phase of primary

education, teachers and parents are the principal informants given the developmental

considerations relating to child self-report. However, from the age of around 7 (e.g. the beginning

of Key Stage 2 in England) child self-report is fully integrated and given equivalent weighting to

teacher and parent views. Challenges of reach, accessibility and diversity are not ignored, but

instead embraced from the outset. The screener is available in multiple languages, has a low

reading age, and is available offline in hard copy for those without internet access. Among so-

called ‘hard to reach’ groups, local support, incentives and advocates are used to promote truly

universal access and engagement. Clear information about the purpose and goals of screening is

communicated in a sensitive, culturally competent manner. Of particular note is the foregrounding

of information regarding use and sharing of data to ensure informed consent.

The screening tool itself is a bespoke, publically available measure that operates with a two-stage

system that begins with simple nomination and is followed up with a robust standardised measure.

This means that teachers can complete the initial stage in a very short period of time. The second

26

stage is applied to those children flagged through the initial nomination. The standardised

measure used at this point balances psychometric robustness with practical considerations, and is

used to produce a more detailed mental health profile that can be used to inform referral for

intervention, while also filtering out any false positive cases identified through nomination. The

measure reflects a dual factor model of mental health. Teacher data is triangulated with that

provided by parents and children to optimise the sensitivity and specificity of the tool. Weighting of

perspectives is dynamic and reflects the evidence base regarding different informants (e.g. under-

or over-reporting of a given informant for a specific domain of functioning when the child is at a

particular developmental phase). The tool is underpinned by a very large, representative

normative sample. The process takes into account individual and contextual differences in

producing classifications. Resultant information on MHD risk status in addition to areas of adaptive

functioning is provided to relevant stakeholders (including external agencies and services as

appropriate) to enable a swift, shared decision-making process regarding referral for intervention

that takes into account areas of strength and resilience that may be capitalised upon. Referrals

are made as early as possible following screening and are supported by a reformed system of

school mental health provision in which practice is evidence-informed and a central hub for

external services that can provide additional support and/or more intensive, indicated intervention

as required (e.g. child and adolescent mental health teams). Data produced through the screening

process is also used in aggregated forms at class and school levels to monitor and evaluate

provision, inform school improvement planning and provide evidence in support of school

inspections. Downstream monitoring of effects in terms of reductions in the incidence and/or

severity of MHD over time, changes in proportion of unmet need and time taken for children and

young people to receive appropriate services are used as robust markers to determine the utility of

universal screening as a means through which to improve the mental health of children and youth.

27

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