Social Support, Risk-Level and Safety Actions Following Acute Assessment of Suicidal Youth

P1: FTK/FNM/FVI/ZCC P2: FLF/FNV QC: FTK

Journal of Youth and Adolescence [jya] PL155-45 July 21, 2000 12:31 Style file version Nov. 19th, 1999

Journal of Youth and Adolescence, Vol. 29, No. 4, 2000

Social Support, Risk-Level and Safety ActionsFollowing Acute Assessment of Suicidal Youth

Deborah Smith1 and Ruth Anderson2

Received April 26, 1999; accepted February 2, 2000

The purpose of the pilot study was to examine the interrelationship between so-cial support, risk-level, and safety actions for 2 groups of suicidal adolescents(50 attempters and 50 ideators), who had presented for an acute assessment at anoutpatient mental health service. A social support model was proposed in which itwas thought that information related to patients’ social support would impact uponthe types of risk-level and safety actions made by clinicians. Data was collectedfrom a total of 100 patient files, utilizing the acute assessment reports (e.g., reportsassessing risk of self-harm). Findings show that groups differed significantly on in-dices of negative support severity, positive support, and risk-level. Limited supportwas found for the proposed social support model. Limitations and implications forfuture research are discussed.

The authors would like to acknowledge the assistance of Associate Professor Andrew Trlin, MasseyUniversity; Duncan Hedderley, Research Officer, Statistics Research and Consulting Service, MasseyUniversity; Dr. Ted Drawneek, Computing Services, Massey University; Associate Professor FrankDeane, University of Wollongong, Australia; Kirsty Louden, Clinical Psychologist, Child, Adolescentand Family Service, Wellington, New Zealand; and management and staff at the Porirua and WellingtonChild, Adolescent and Family Clinics (CAFS) in New Zealand.

1Ph.D. Candidate, School of Social Policy and Social Work, Massey University, New Zealand. MSW,University of Toronto, Ontario, Canada; BAH, Queen’s University, Kingston, Canada. Clinical socialworker at the CAFS service in Wellington, New Zealand. Rresearch interests include mental healthissues in young people. To whom correspondence should be addressed at School of Social Policyand Social Work, Massey University, Private Bag 11 222, Palmerston North, New Zealand; e-mail:[email protected].

2Academic Director, College of Humanities and Social Sciences, and Senior Lecturer, School of SocialPolicy and Social Work, Massey University, New Zealand. Ph.D., Massey University, New Zealand;M.Ed. (Couns.) (Disn.), University of Canterbury, New Zealand; B.Sc., University of Canterbury,New Zealand. President of New Zealand Association of Counsellors (1991–1993). Current researchinterests in counselling and psychotherapy, constructivist psychology and practice ethics.

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452 Smith and Anderson

INTRODUCTION

It is a well-known fact that New Zealand has one of the highest youth suiciderates in the world for those aged 15–24 years (New Zealand Ministry of Health,1998). It is this fact that has spurred the increase in both interest and research on thisparticular issue. Of specific interest in the clinical community is how cliniciansboth assess and treat this at-risk population (New Zealand Ministry of Health,1998).

The purpose of the study was 2-fold: (1) to provide the foundation for alarger study and (2) to examine the interaction between social support, risk-level,and safety actions made by clinicians for suicidal young people. This study wasspecifically focused on how social support is assessed by clinicians and its impactupon clinicians’ decisions for patient risk-level and their decision(s) to ensurecontinued patient safety. The variables of social support, risk, and safety werecompared across 2 groups: attempters and ideators. This distinction was made asprevious literature has indicated that attempters and ideators differ on some riskfactors (support) despite engaging in behavior along the same continuum (Dubowet al., 1989; Fergusson and Lynskey, 1995; Koskyet al., 1990).

Social Support Typologies

There appears to be a lot of confusion and a lack of consensus regardinga theoretical definition of social support (Vaux, 1988). Many theoretical mod-els of social support argue that social support is a complex metaconstruct con-sisting of an interaction among a variety of terms: social support network re-sources, supportive behavior, and subjective appraisals of support (House andKahn, 1985; Pierceet al., 1996; Thompson, 1995; Vaux, 1988). Most typologiesappear on a basic level to distinguish between 2 different types of supportive be-havior: emotional support and instrumental support (House and Kahn, 1985; Pierceet al., 1996; Thompson, 1995; Vaux, 1988). Emotional support refers to behaviorsthat communicate to an individual that he/she is cared for and loved (Sarasonet al., 1983). Instrumental support refers to behaviors that provide assistancein task-directed coping efforts (e.g., practical, tangible behaviors) (Pierceet al.,1996).

The difficulty with this dichotomy is that a display of instrumental support(e.g., lending money) may also be interpreted as an expression of love or caring.Thus, what initially might have appeared to be instrumental support could also beconsidered a display of emotional support (Pierceet al., 1996).

The overlap existing among the many different functions of social support(e.g., socialization, emotional, advice, guidance etc.) may account for the substan-tial correlations observed across subscales intended to assess discrete functionsof support (Pierceet al., 1996). For this reason, the main typology chosen forsupportive behavior in this study is that of emotional support.



Suicide Assessment in Youth 453

Risk-Level Assessment

Once a clinician has obtained all relevant background information, the clin-ician must decide whether the patient is at risk of committing suicide (Bermanand Jobes, 1991; Mariset al., 1992; Sommers-Flanagan and Sommers-Flanagan,1995); often taking into account many risk variables, such as abusive or violentupbringings (Beautraiset al., 1996), poor social support (Campbellet al., 1993;D’Attilio et al., 1992; DeMan and Leduc, 1995; Eskin, 1995; Whatley and Clopton,1992), a family or individual history of psychopathology (particularly if comorbid)or both (Beautraiset al., 1996; Brentet al., 1993; Clark, 1993; Clark and Mokros,1993; Fergusson and Lynskey, 1995; Hollis, 1996; Koskyet al., 1990), and drugand alcohol abuse (Brentet al., 1993; Koskyet al., 1990). Taking all these riskfactors into account, Berman and Jobes (1991) propose that the greater the numberof risk factors present, the greater the likelihood is of that person being at risk offuture suicidal behavior.

One risk factor this study is particularly interested in is that of poor socialsupport (Maris, 1997). It has been shown that suicidal adolescents tend to comefrom backgrounds characterized by childhood adversity (e.g., parental separation,poor parental relationships, parental violent behavior, inadequate parenting, familycommunication problems, abuse) and family conflict (Beautraiset al., 1996; Brentet al., 1993; Hollis, 1996; Koskyet al., 1990). Moreover, it has been reportedthat suicidal adolescents tend to perceive and report less supportive relationshipswithin their family unit (Campbellet al., 1993).

It is important to note that risk-level assessment is not an exact science andincludes the use of standardized clinical instruments or clinical interviews or both(Havens, 1999; Sommers-Flanagan and Sommers-Flanagan, 1995; Yufit, 1989),with a focus on exploring suicidal thoughts and behaviors and the psychologicaldimensions of suicide risk (Clark, 1998). Ideally, researchers suggest that risk-levelassessment is best conducted by a specialized Suicide Assessment Team (SAT)utilizing both structured (e.g., rating scales) and unstructured (e.g., interviews)techniques (Yufit and Bongar, 1992).

Among the many identified risk factors that put youth at risk of self-harm,this study was focused upon investigating whether the risk factor of social supporthad any impact on the risk-level decision made by a clinician.

Clinical Decisions for Safety in Suicide Research

Following an assessment of risk, the clinician must decide, along with theyoung person, upon a variety of safety actions so as to ensure continued safetyof the client. These safety actions include such things as hospitalization, referralsto external agencies (e.g., Children, Young Persons and Their Families Servicesor equivalent), and education about limiting access to methods (e.g., locking cup-boards containing prescription medicine).




With respect to safety actions, there is little research available. However,2 studies have been conducted, looking primarily at the safety action of hospi-talization. Both of these studies used similar methodology (case vignettes) anddesign, but different samples of clinicians (e.g., psychiatric medical residents vs.experienced child–adolescent clinicians). In both cases, participants had to readcase vignettes and decide whether they would hospitalize the patient or not. Theyused a total of 6 known variables related to lethality of attempt (including so-cial support) and varied them to produce a total of 64 vignettes. They found thatpsychiatric residents tended to recommend hospitalization more frequently thandid experienced child–adolescent clinicians (Dickeret al., 1997; Morrisseyet al.,1995).

The next section examines how the concepts of social support, risk-level, andsafety actions interrelate within a proposed social support model.

Social Support Model

Based on theoretical material derived from the main prevailing models ofsocial support (House and Kahn, 1985; Pierceet al., 1996; Thompson, 1995;Vaux, 1988), the present project was interested in investigating the validity ofthe following proposed model for assessment of suicide risk (see Fig. 1). Basedon the previous research, it is believed that social support has a role in affectinghow clinicians make decisions about a client’s risk-level. For example, if there is aminimal social support network, as well as minimal supportive behavior combinedwith negative appraisals of that support, then the clinician will probably be morelikely to rate that client at a high risk-level. The reverse of this situation wouldprobably result in the clinician rating the client at a lower risk-level. Of course,all other risk factors would need to be taken into consideration when the decisions

Fig. 1. Social support model.




are made. This scenario was well illustrated in the Dickeret al. (1997) and theMorrisseyet al. (1995) study.

It is also believed that information about the level of a client’s social supportwill have an impact on the decisions that are made with respect to ensuring safetyon an outpatient basis. For example, if someone has few persons in his/her supportnetwork and minimal supportive behavior available to him/her, as well as a neg-ative perception of that behavior provided (also known as negative support), thenas clinicians, decisions made in discharge planning will primarily focus aroundstrengthening that patient’s support system (Berman and Jobes, 1991).

The presence of positive support (e.g., a behavior that is interpreted by therecipient as providing helpful support such as someone listening to another’s prob-lems) versus the presence of negative support (behavior that is perceived by therecipient as unhelpful or destructive such as abuse or conflict) has been shown togreatly influence suicidal behavior. For example, Rubensteinet al. (1998) foundthat suicidal youth perceptions of family cohesiveness alleviated risk in nonintactfamilies whereas perceived family destructiveness was found to increase risk innonintact families.

In summary, this project is interested in examining to what degree the type andlevel of social support impacts upon clinicians’ decision for risk-level and safetyactions taken. This study expects to find for the attempter group, a greater degreeof severity reported in the case of negative support, higher risk-level assessments(medium to high risk), and consequently, a greater number of safety actions takencompared to the ideator group. Based on the aforementioned model, it is thoughtthat the first 2 factors (support and risk) will influence final safety actions madeby clinicians following acute assessment of suicidal youth.

METHOD

Sample

Patient records based on New Zealand male and female adolescents ofEuropean descent ranging from 13 to 17 years of age were used for this pilotstudy. These adolescents, as a result of either attempting or ideating about suicide,presented at 2 Child, Adolescent and Family Services (CAFS) in a large city overthe last several years for an acute assessment of risk of self-harm.

Fifty adolescents comprised the attempter group and another 50 adolescentscomprised the ideator group. Attempters were defined as those who had both theintent to die, as well as sufficient lethality in the method used to accomplish that in-tent, but either failed or were rescued (Reynolds and Mazza, 1994, p. 528). Ideatorswere defined as those persons presenting as having cognitions about wanting todie including thoughts and ideas about death, suicide, and serious self-injuriousbehavior and more specifically including thoughts related to the planning, conduct,




and outcome of suicidal behavior (Reynolds and Mazza, 1994). These definitionsfor assignment by group were also based on past research examining the char-acteristics of both attempters and ideators (Dubowet al., 1989; Fergusson andLynskey, 1995; Koskyet al., 1990). The researchers were unable to collate dataon intention of self-harm due to this information largely being unavailable in therecords: clinicians often mentioned in reports only whether a client was initiallyassessed as a result of ideation or attempt. As such, the groups were differentiatedaccording to the definitions proposed by the aforementioned research. Informationon diagnosis and past attempts/ideation were collected so as to examine whetherthere were any other identifiable ways of differentiating the groups. This issue ofgroup differentiation is addressed on a larger scale in later discussion.

Setting

Case records (acute assessment reports only) were reviewed by the researcherand second coder in a private office on site at the Puketiro and Wellington CAFSClinics.

Measures

An elaborate coding system was used for this study. The following is a briefsummary of the measures as operationalized based on previous research:

1. Negative Social Support. This was defined as encompassing (a) the ex-istence of dysfunctional or unhelpful support or (b) the nonexistence ofsupport (e.g., neglect). Some examples of negative social support includeany references in reports to people who abuse the young person, peoplewho the young person cannot trust, and people who communicate withthe young person in a dysfunctional way (see e.g., Beautraiset al., 1996;Campbellet al., 1993; Hollis, 1996; Koskyet al., 1990; Sarasonet al.,1983; Vauxet al., 1987).

2. Positive Social Support. This was defined as encompassing support that isperceived as helpful. Some examples of positive support include referencessuch as having someone who will listen when you need to talk about aproblem to having someone to rely on for help (see e.g., Rubensteinet al.,1998; Sarasonet al., 1983; Vaux, 1987).

Both positive and negative social support were measured as beingeither present or not present in the file.

3. Negative Support Severity. This was defined as evidence in the report ofdifferent levels of negative support behavior (e.g., a fight to somethingmore extreme such as abuse), with consequential varying levels of impair-ment in school, work, home or relationships or a combination of these.Negative support severity was measured by using a low, medium, and high




scale, with low representing less severe psychosocial impairment, mediumrepresenting moderate psychosocial impairment, and high representingmore severe psychosocial impairment. Each of these definitions takes intoaccount both the type of conflict (e.g., fight, argument, or abuse) as wellas its impairment on the recipient (Chaffinet al., 1997; Straus, 1979).

4. Risk-Level(a) Children’s Global Assessment Scale (CGAS)—for children 4–16 years

of age. Its values range from 1 (representing the most functionallyimpaired child) to 100 (representing the healthiest). The instrumentcontains behaviorally oriented descriptors at each anchor point, whichdepict behaviors and life situations applicable to the age range of 4–16 years over the last month. The CGAS has good test–retest reliabil-ity (r = .85), has discriminant and concurrent validity, and is widelyused as a clinical instrument aimed at measuring overall severity ofdisturbance (Shafferet al., 1983).

(b) Risk-level measurement based on the notion of the SAD PERSONSscale (Pattersonet al., 1983) and Sommers-Flanagan and Sommers-Flanagan (1995) typology. It was measured by using low, medium,and high risk categories and operationalized accordingly using a scalefrom 1 through to 3.

5. Safety-Oriented Actions. The following is a list of the most common formsof safety-oriented actions made following an acute assessment so as to en-sure continued safety. The list is as follows: hospitalization, referral toCYPFS, respite care (e.g., overnight nursing care), provision of emer-gency numbers, time-out placement for a night, time-out placement forseveral days, limiting of method availability, medication, internal referralto CAFS for specialist consult (e.g., psychological assessment), referralto external agency or organization (e.g., Youth Aid Officer), next plannedappointment, and next planned telephone contact. These items were mea-sured as being either present or not present in the file.

Procedure

This study was an archival research project involving the review of acuteassessment reports for those adolescents who presented to the CAFS service for asuicide assessment. Once ethical approval was granted by both the Massey Univer-sity Human Ethics Committee and the Wellington Ethics Committee, the data col-lection phase began. Patient files were located using the CAFS computer database,which indicates whether patients were seen for a suicide assessment. Cases wereselected consecutively from the previous 3 years until 100 patient files had beenchosen according to sample criteria; 50 of those being of ideators and 50 being ofattempters.




Confidentiality and anonymity were ensured by (1) coding all records with3-digit numbers, (2) photocopying and deleting identifiable information from thereports, and (3) coding the reports in a private office and placing all reports in alocked filing cabinet at the end of the day.

RESULTS

All data were entered into the SPSS 8.0 program and tests involved the useof chi-square,t-tests, and loglinear model analysis. Reliability was computed forall numerical and categorical data. Results indicated that the data collected wasreliable, with 1 exception. The CGAS scores for the attempter group were shownto be unreliable using the following tests: Kendall test (p < .001), Wilcoxon test(p < .001), and the Cronbach test (p < .001). As such, any results found to besignificant utilizing the CGAS for the attempter group need to be interpreted withsome caution. The reliability statistics computed for the categorical data indicatedKappa values ranging from .79 to 1.00.

Sample Characteristics

With respect to sample characteristics, no differences were found between thegroups on the following indices: diagnosis (axis 1 through to 4), alcohol and drugmisuse/abuse, and previous attempts/ideation. The effect of age was not statis-tically significant (t(2, 98)= −.77, p > .05). There was a significant differencebetween the groups for gender (χ2(1,N = 100)= 3.93, p < .05). Overall, therewere more female subjects (n = 71) than male subjects (n = 29). The results forthese descriptive statistics can be found in Table I.

Table I. Demographic Statistics and Chi-Square Analyses

Attemptera Ideatorb

Demographics Yes (%) n Yes (%) n χ2 (df = 1)

DSM axis 1 74.0 37 80.0 40 .51DSM axis 2 2.0 1 6.0 3 1.04DSM axis 3 12.0 6 8.0 4 .44DSM axis 4 66.0 33 56.0 28 1.05Comorbidity 32.0 16 24.0 12 .79Alcohol and drug abuse 20.0 10 10.0 5 1.96Alcohol and drug misuse 8.0 4 16.0 8 1.52Previous attempts 46.0 23 46.0 23 0.00Previous ideation 40.0 20 52.0 26 1.50Psychiatric history 26.0 13 48.0 24 5.19∗

an = 50.bn = 50.∗p < .05.




Table II. Support Variables and Chi-Square Analyses by Group

Attemptera Ideatorb

Support Yes (%) n Yes (%) n χ2 (df = 1)

Negative support 84.0 42 88.0 44 .33Positive support 58.0 29 36.0 18 4.86∗

an = 50.bn = 50.∗p < .05.

Social Support

With respect to negative support, no differences were found between thegroups. There were 42 patient files (n = 50) for the attempter group, which indi-cated the presence of negative support in the patient’s life compared to 44 patientfiles (n = 50) for the ideator group (χ2(1,N = 100)= .33, p > .05) (see Table II).After recoding the medium and high categories, it was found that there was a sig-nificant difference between the attempter and ideator groups for the presence ofnegative support severity (χ2(1,N = 100)= 3.85,p < .05). For negative supportseverity, it was found that 15 patients (N = 44) in the ideator group scored in thelow category whereas 27 scored in the medium–high category. In contrast, it wasfound that 25 patients (N = 42) in the attempter group scored in the low categorywhereas 19 scored in the medium–high category. With respect to positive support,there was a significant difference found at the .05 level between the ideator andattempter groups (χ2(1,N = 100)= 4.857,p < .05) (see Table II).

Risk-Level

With respect to risk, there were also significant differences found betweenboth groups on the CGAS and Risk Measures. For the CGAS, the mean scale scorefor the attempter group was 45.50 whereas the mean scale score for the ideatorgroup was 55.48 (t(2, 98)= −4.12, p < .001). The scale scores were then recodedinto their subsequent impairment categories ranging from 1 (representing very highimpairment) through to 10 (representing extremely minimal impairment). Afterrecoding, there was still a significant difference found between the attempter andideator groups on the CGAS (χ2(3,N = 100)= 13.03,p < 0.05) (see Table III).For the risk measure, the researcher recoded the medium and high categoriesinto 1 category labeled medium–high. This recoding was necessary due to lowcell counts and so as to make the analysis more meaningful. After recoding therisk categories, it was found that there was a significant difference between bothgroups (χ2(1,N = 100)= 71.59,p < .001) (see Table IV), with the attemptergroup receiving higher risk ratings than the ideator group.




Table III. CGAS Recode Statistics and Chi-Square Analyses by Group

Attemptera Ideatorb

CGAS recode Yes (%) n Yes (%) n χ2 (df = 1)

1–4 32.0 16 10.0 5 13.03∗5 28.0 14 16.0 86 24.0 12 52.0 267 16.0 8 22.0 11

an = 50.bn = 50.∗p < .001.

Table IV. Risk-Level Measure Recoded and Chi-Square Analysis by Group

Attemptera Ideatorb

Risk Yes (%) n Yes (%) n χ2 (df = 1)

Low 2.0 1 86.0 43 71.59∗Medium-high 98.0 49 14.0 7

an = 50.bn = 50.∗p < .001.

Safety Actions

Finally, for the individual safety actions, it was found that the only actionwhich differed significantly between both groups was that of CYPFS (χ2(1,N =100)= 5.98,p < .05). Despite the difference, it was not 1 of the more commonactions taken, as can be evidenced in Table V. The most common actions forboth groups were provision of emergency numbers, arranging a next appoint-ment, and medication. For each patient, the researcher totalled the number ofsafety actions made per file. As a result of low cell counts, the researcher re-coded total safety actions 5 through to 9 into a new category labeled 5 or more.However, even after looking at the total number of actions, there were no signifi-cant differences found between the groups (χ (4,N = 100)= 1.29,p > .05) (seeTable VI).

As regards the more complex loglinear analysis, a total of 6 different analyseswere conducted using the following variables: group, negative support, negativesupport severity, positive support, risk, and total safety actions. A loglinear analysiswas conducted so as to examine whether there were any 2, 3, or 4-way interactionsbetween the variables of interest. As mentioned previously, the variables of negativesupport severity, risk, and total actions were recoded due to low cell counts. Theanalysis revealed only partial support for the model proposed earlier. Only 2-way




Table V. Safety Actions and Chi-square Analysis by Group

Attemptersa Ideatorsb

Safety actions Yes (%) n Yes (%) n χ2 (df = 4)

Hospital 10.0 5 8.0 4 .12CYPFS 2.0 1 16.0 8 5.98∗Respite 2.0 1 0.0 0 1.01Emergency numbers 48.0 24 38.0 19 1.02Time-out 12.0 6 4.0 2 2.17Time-out—Night 0.0 0 2.0 1 1.01Time-out—Day 2.0 1 0.0 0 1.01Limiting method 14.0 7 12.0 6 .09Medication 36.0 18 36.0 18 .00Referral—CAFS 10.0 5 14.0 7 .38Referral—External 18.0 9 16.0 8 .07Next appointment 94.0 47 96.0 48 .21Next telephone call 4.0 2 14.0 7 3.05Liaison with GP 12.0 6 8.0 4 .44Tell parents 4.0 2 4.0 2 .00People to talk to 8.0 4 4.0 2 .71School liaison 10.0 5 8.0 4 .12Promise not to harm 2.0 1 10.0 5 2.84Someone watching over child 12.0 6 18.0 9 .71

an = 50.bn = 50.∗p < .05.

Table VI. Total Number of Safety Actions Recoded and Chi-Square Analysis by Group

Attemptersa Ideatorsb

Total number of safety actions Yes (%) n Yes (%) n χ2 (df = 4)

1 22.0 11 22.0 11 1.292 22.0 11 18.0 93 20.0 10 22.0 114 26.0 13 20.0 105–9 12.0 6 18.0 9

an = 50.bn = 50.

interactions were found to be significant between group and negative severity(p < .001), group and positive support (p < .001), group and risk (p < .001),and group and CGAS recoded (p < .05). This finding means that differences werefound between the groups on the following indices: negative support severity,positive support, risk, and CGAS recoded. These differences were already indicatedpreviously, as can be seen in the reported chi-square tests for the aforementionedindices.




DISCUSSION

The purpose of the pilot study was mainly to examine the interrelationshipbetween the variables of social support, risk-level, and safety actions for 2 groups(attempters and ideators) of suicidal youth presenting for an acute assessment atan outpatient mental health service. Using chi-square,t-tests, and loglinear modelanalyses, several significant differences were found between the 2 groups mainlyon the variables of support and risk. Using loglinear model analysis, only partialsupport for the proposed social support model was established. As discussed,reliability was particularly good on all indices except for the CGAS ratings for theattempter group.

As mentioned previously, there were no differences between the groups onsample characteristics except with respect to gender. Interestingly enough, basedon the information from clinicians’ reports, the results indicated that the majorityof the attempter group had reported some form of ideation in the past and that themajority of the ideator group had reported some form of an attempt in the past.It could well be that despite previous findings which indicate that the 2 groupsare different (Fergusson and Lynskey, 1995; Pelkonenet al., 1997), they actuallyare quite similar because it has been indicated that they function along the samecontinuum of behavior (Dubowet al., 1989; Kovacset al., 1993; Pearce and Martin,1994; Pelkonenet al., 1997).

Based on the information obtained from clinicians’ reports, there were somedifferences noted between the groups with respect to negative support severityand risk. Past findings seem to indicate that there is a greater degree of negativesupport present in attempters than in ideators (Dubowet al., 1989; Fergusson andLynskey, 1995; Koskyet al., 1990). The present research seemed to support thisfinding in that attempters received greater negative support severity ratings thanideators did. This finding makes “clinical sense” in that exposure to more severeforms of negative support can trigger more severe forms of suicidal behavior (e.g.,attempting vs. thinking) (Berman and Jobes, 1991).

With respect to positive support as indicated within clinicians’ reports, theresults indicated that there was a significantly greater presence of positive supportfor the attempter group compared to that for the ideator group; a finding whichis in opposition to that hypothesized. This unexpected finding could be a resultof clinician bias in assuming that ideators are exposed to less extreme forms ofnegative support, negating the need to inquire about positive support. As such,this situation might lead to a failure to record that which may be present and anovercompensation to record positive support for those who attempt. However, thenature of this relationship will be more rigorously tested in the principal study so asto help understand whether any other factors are affecting this unexpected result.

Overall, the results indicated support for the hypothesis that the attemptergroup would receive higher risk-level and impairment ratings than the ideator group




would. This finding seems to support theoretical and empirical material availableon suicide risk and assessment (see e.g., Berman and Jobes, 1991; Pattersonet al.,1983; Reynolds and Mazza, 1994). This result seems reasonable in that thosewho attempt are generally exposed to a greater degree of negative stressors thanthose who ideate, thereby possibly putting this group at greater risk (Berman andJobes, 1991; Dubowet al., 1989; Fergusson and Lynskey, 1995; Koskyet al.,1990). However, it is important to note that other factors such as biological andpsychosocial factors may also be impacting upon risk-level.

The results indicated a lack of support for the hypothesis that the attemptergroup would receive a greater number of safety actions than the ideator groupwould. There were no differences found within this variable except for the ac-tion labeled “CYPFS referral.” However, this finding needs to be interpreted withcaution due to the low cell counts in the attempter group.

As mentioned previously, only partial support for the proposed social supportmodel was found. According to the information coded from patient files, it wasfound that the significant interactions from the loglinear model analysis confirmedthe earlier findings reported (1) that the attempter group received higher negativesupport severity ratings than the ideator group did, (2) that the members of theattempter group were rated to be at a higher risk-level than those of the ideatorgroup, and (3) that clinicians’ reports indicated a greater presence of positivesupport for the attempter group than for the ideator group.

Some of the limitations found within this study can be noted as follows:(1) this sample was chosen from a very specific outpatient sample, thereby makingit difficult to generalize, (2) the sample was chosen consecutively rather thanrandomly, (3) difficulties were experienced in interpreting client file reports, asdiscrepancies may have existed between the content and what was actually reportedduring the session, and (4) there are difficulties with archival research in thatclinicians may have recorded only that which they felt was important, and as such,some of the needed data for this study could have been omitted from the reports.

A final limitation that needs to be addressed is that of group differentiation.Due to the limited availability of information in the reports, the researchers wereconstrained in the way that the groups were defined. Information on intention ofself-harm was largely unavailable from the records. Information on such itemsas diagnosis and past/recent attempts or ideation or a combination of these wascollected, but failed to show any significant differences between the groups. Theabove restraints were seen to be a limitation of the archival research design chosenfor this study. Regardless, the definitions for attempting and ideating were ofuse because this study found many differences across both groups (e.g., negativesupport severity, risk).

The most significant outcome of this project is its ability to describe andexamine a clinical adolescent outpatient sample in the area of mental health in NewZealand. Besides the extensive work completed by Beautraiset al. (1996, 1998),




which mainly looked at hospital admissions for youth at risk, very little work hasbeen completed in this area. Secondly, this pilot project is useful in that it providesthe framework for a larger study and helps to identify where the possible gaps liewithin the research. For example, it may be possible to more directly examine theeffects of support on decision-making by controlling for support in sample casevignettes and asking clinicians to make decisions regarding risk and safety. Byisolating the variable of support, it should assist in more directly examining therole it plays in clinician decision-making.

The findings from the pilot project are useful to the research area of youthsuicide. Despite the limitations, this project may help to advance this area ofresearch and provide suggestions for further refinement and revision of currentassessment procedures.

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Social Support, Risk-Level and Safety Actions Following Acute Assessment of Suicidal Youth