Psychiatric Rehabilitation Journal 2015, Vol. 38, No. 3, 210 –217
© 2015 American Psychological Association 1095-158X/15/$12.00 http://dx.doi.org/10.1037/prj0000129
The Relationship Between Cognition, Job Complexity, and Employment Duration in First-Episode Psychosis Emma Caruana
Susan Cotton and Eóin Killackey
The University of Melbourne
Orygen, The National Centre of Excellence in Youth Mental Health, Parkville Victoria, Australia, and The University of Melbourne
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Kelly Allott Orygen, The National Centre of Excellence in Youth Mental Health, Parkville Victoria, Australia, and University of Melbourne Objective: To investigate the relationship between cognition and employment duration in firstepisode psychosis (FEP), and establish if a “fit” between cognition and job complexity is associated with longer employment duration. Method: This study involved secondary data analysis of a subsample of FEP individuals (n ⫽ 65) who participated in a randomized controlled trial comparing Individual Placement and Support plus treatment as usual (TAU), versus TAU alone, over 6 months. A cognitive battery was administered at baseline and employment duration (hours) and job complexity in the longest held job over 6 months were measured. Results: Factor analysis with promax rotation of the cognitive battery revealed 4 cognitive domains: (a) attention and processing speed; (b) verbal learning and memory; (c) verbal comprehension and fluency; and (d) visual organization and memory (VO&M). The final hierarchical regression model found that VO&M and job complexity independently predicted employment duration in longest held job; however, the “fit” (or interaction) between VO&M and job complexity was not significant. Conclusions and Implications for Practice: These findings suggest that VO&M and job complexity are important predictors of employment duration, but it is not necessary to ensure VO&M ability matches job complexity. However, there are limited comparative studies in this area, and other aspects of the person– organization fit perspective may still be useful to optimize vocational outcomes in FEP. Keywords: cognition, job complexity, employment duration, supported employment, first-episode psychosis Supplemental materials: http://dx.doi.org/10.1037/prj0000129.supp
Supported employment (SE) is the leading evidence-based vocational intervention for severe mental disorders (Killackey, Jackson, Gleeson, Hickie, & McGorry, 2006). Individual Placement and Support (IPS) is a highly defined form of SE (Drake, Bond, & Becker, 2012) with effectiveness in improving employment outcomes in chronic psychotic disorders (e.g., Drake et al., 2012) and FEP (Killackey, Jackson, & McGorry, 2008; Nuechterlein et al., 2008). We recently completed a randomized controlled trial involving 146 FEP participants and found that 6 months of IPS resulted in significantly higher rates of employment and education (88.1%), compared to treatment as usual (TAU; 72.1%; p ⫽ .023; Allott et al., 2013; Killackey et al., 2012). However, there was no difference between IPS and TAU in employment duration (mean hours worked) over the 6-month period (247.2 vs. 241.5; p ⫽ .927; Allott et al., 2013). Further, in chronic schizophrenia, over 50% of individuals experience unsatisfactory job terminations (Bond, Drake, & Becker, 2008), and have difficulties maintaining employment after SE (McGurk & Meltzer, 2000). These findings raise issues about how to optimize vocational outcomes and call for a better understanding of enhancing employment duration in psychosis.
Unemployment is a significant problem for individuals with first-episode psychosis (FEP). Approximately half of young people are unemployed by the time they seek help (Fraser, Berger, Killackey, & McGorry, 2006) and express vocational rehabilitation as a major treatment goal (Iyer, Mangala, Anitha, Thara, & Malla, 2011). Employment rates decline further in the year following admission for FEP (Marwaha & Johnson, 2004). Unsurprisingly, clinical guidelines for FEP strongly recommend early vocational rehabilitation (Early Psychosis Guidelines Writing Group, 2010).
This article was published Online First April 6, 2015. Emma Caruana, Melbourne School of Psychological Sciences, The University of Melbourne; Susan Cotton and Eóin Killackey, Orygen, The National Centre of Excellence in Youth Mental Health, Parkville Victoria, Australia, and Centre for Youth Mental Health, The University of Melbourne; Kelly Allott, DPsych, Orygen, The National Centre of Excellence in Youth Mental Health, and Centre for Youth Mental Health, The University of Melbourne. Correspondence concerning this article should be addressed to Kelly Allott, 35 Poplar Road, Parkville Victoria, Australia 3052. E-mail: [email protected] 210
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COGNITION AND EMPLOYMENT DURATION IN FEP
Cognition is a key predictor of vocational outcomes in established schizophrenia generally (Bryson & Bell, 2003; McGurk & Mueser, 2004), and within the context of vocational rehabilitation (Lysaker, Bell, & Beam-Goulet, 1995; Lysaker, Bryson, Davis, & Bell, 2005; McGurk & Mueser, 2004; McGurk, Mueser, Harvey, LaPuglia, & Marder, 2003). However, studies in FEP have reported mixed findings (Allott, Liu, Proffitt, & Killackey, 2011), with several studies finding a significant relationship between cognition and vocational functioning (Dickerson et al., 2008; Nuechterlein et al., 2011; Tandberg et al., 2011), and others reporting no relationship (Verdoux, Liraud, Assens, Abalan, & van Os, 2002). Allott et al. (2011) identified that these mixed findings may be due to lack of control for additional predictor variables, such as employment history, general intellectual functioning (IQ) and negative symptoms, and recommended future studies control for these. A further methodological issue which may explain the variation found across studies is the large variation in measurement of cognition (Allott et al., 2011). The use of multiple neuropsychological tests that measure functioning across several cognitive domains, and the employment of data reduction techniques to validate which cognitive domains each test represents, may improve the reliability of findings in relation to cognition and vocational outcomes (Bowie et al., 2008; Gladsjo et al., 2004; Jaeger, Czobor, & Berns, 2003). The use of data reduction techniques is also important given the multifactorial nature of some of these tests; with some tests measuring multiple cognitive domains, rather than mapping neatly onto one domain (Kaplan, 1990). In addition to methodological issues, none of these studies investigated the relationship between cognition and employment duration in FEP. The predictors of employment attainment may differ from the predictors of employment tenure (Gold, Goldberg, McNary, Dixon, & Lehman, 2002; McGurk et al., 2003). We recently found that within the context of IPS, although cognition did not predict whether participants gained employment, higher visual organization and memory predicted greater employment duration (hours) over 6 months (Allott et al., 2013). This leads to the question of whether job tenure may be improved if the cognitive ability of an individual is matched with the cognitive complexity of their job. Remediating or accommodating cognitive deficits may be necessary to improve vocational outcomes such as employment duration (Allott et al., 2013; McGurk & Mueser, 2003). Cognitive remediation within a supported employment program has shown promise in further improving vocational outcomes (McGurk, Mueser, Feldman, Wolfe, & Pascaris, 2007; McGurk, Mueser, & Pascaris, 2005). With regard to accommodating cognitive deficits, McGurk and Mueser (2003) proposed two models instrumental to the success of SE: the compensatory model and the environmental niche model. The compensatory model hypothesizes that SE is effective when it compensates for the cognitive and symptom impairments associated with psychotic disorders that would otherwise interfere with work (e.g., minimizing cognitive fatigue by scheduling regular work breaks). Conversely, the environmental niche draws on the strengths model of case management (Rapp, 1998). Here, SE is effective when it helps clients find work in settings where their cognitive impairments and symptoms pose minimal interference, and where natural supports (e.g., coworkers, family, friends) may be harnessed to minimize their negative
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effects on work (e.g., matching job complexity with an individual’s cognitive ability). Job complexity is the cognitive complexity of job activities, such as whether a job has higher (e.g., office manager) or lower (e.g., typist) cognitive demands. Employment success for individuals with severe mental illness may be assisted through adopting a person– organization fit perspective, but this has yet to be explored (Henry, 2004). In chronic schizophrenia, executive functioning is significantly associated with job complexity within the context of independent employment, but not SE (McGurk & Mueser, 2003). Those independently employed and with lower executive functioning had less cognitively complex jobs; they had found a natural environmental niche where deficits were accommodated. The lack of association between executive functioning and job complexity in SE suggests that SE was compensating for deficits, rather than matching job complexity to one’s cognitive ability. Thus, matching job complexity and executive functioning may be a prerequisite for clients to move from SE to independent employment (McGurk and Mueser (2003). However, other cognitive deficits may also be important, and a job may have to match an individual’s overall cognitive profile in order for clients to maintain employment as SE support diminishes. Overall, a cognition-job complexity fit may also be important for employment duration, but this has not been previously investigated. The aim of the present study was to investigate the role of cognition in employment duration in young people with FEP, and the potential moderating role of job complexity. We first hypothesized that while controlling for baseline employment, negative symptoms, and premorbid IQ, cognitive domains would predict employment duration in the longest held job over 6 months. Second, we hypothesized that while controlling for the same factors, the “fit” between cognitive domains and job complexity would be associated with improved employment duration in the longest held job over 6 months.
Method Study Design and Participants This study involved secondary data analysis of a randomized controlled trial that was conducted with clients of the Early Psychosis Prevention and Intervention Centre in Melbourne, Australia (see Killackey et al., 2013, for full description of trial). Participants either received IPS plus TAU, or TAU alone, over a 6-month period. Using the Supported Employment Fidelity Scale (Becker, Swanson, Bond, & Merrens, 2011), the IPS delivered in the trial was rated as having “good” fidelity to the model. Inclusion criteria were aged between 15 and 25 years (M ⫽ 20.35, SD ⫽ 2.41), a first episode of a Diagnostic and Statistical Manual-IV-TR-defined psychotic disorder, and an expressed desire to pursue an employment or educational goal. Exclusion criteria included intellectual disability, florid psychosis preventing informed consent, and insufficient English to complete assessments. Of the 146 eligible participants, 11 with premorbid neurological conditions were excluded, leaving 135 participants. These participants were included in the factor analysis described below. The primary analysis was then conducted only on those participants who had worked during the study period. Participants were as-
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CARUANA, COTTON, KILLACKEY, AND ALLOTT
sessed by a research assistant blind to treatment allocation at baseline and 6 months (postintervention).
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Measures Baseline. Baseline assessment covered demographics, including age, gender, marital status, duration of illness, time since diagnosis, educational achievement, employment status (currently in paid employment–yes/no), employment history (months in paid work), and treatment allocation (IPS plus TAU/TAU). Given the literature showing evidence for a relationship between negative symptoms and cognition, the Scale for Assessment of Negative Symptoms composite score (Andreasen, 1984) was included in the current analyses. For the same reason, an estimate of premorbid intellectual functioning was measured using the Wide Range Achievement Test– 4th Edition—reading subtest scaled score (Wilkinson & Robertson, 2006). A neuropsychological battery comprising 11 tests was administered. Subtests of the Wechsler Adult Intelligence Scale—Third Edition (The Psychological Corporation, 1997) were administered, including Similarities, Information, Digit Span, Letter-Number Sequencing and Picture Completion. Other tests were Trail Making Test A and B (TMT; Reitan, 1955), Symbol-Digit Modalities Test (Smith, 1982), Letter Cancellation Task (LCT; Strauss, Sherman, & Spreen, 2006), Rey Auditory Verbal Memory Test (Schmidt, 1996), Rey-Osterrieth Complex Figure Test (Osterrieth, 1944; Rey, 1941), Controlled Oral Word Association Test (COWAT) and the Animal Fluency Test (Strauss et al., 2006). Six-month follow-up. Employment duration in longest held job was measured as the total number of hours worked in the job held for the longest time over the 6-month follow-up period. Due to a moderate positively skewed distribution, this variable was square root transformed (Tabachnick & Fidell, 2012). Job complexity of longest held job was measured using the Social Adjustment Scale-Work Outcome (Weissman & Paykel, 1974). The cognitive complexity of a job is measured on a 5-point scale: (1) none-unskilled laborer; (2) nontechnical skilled work; (3) entry-level skilled work; (4) advanced skilled work; (5) very high-professional occupation. Two independent raters blind to treatment allocation and cognitive functioning rated the jobs gained by participants. When there was a discrepancy between ratings, consensus was obtained through joint discussion.
mended, resulting in more realistic and statistically sound factor structures (Schmitt, 2011). Factor scores were estimated using the recommended Bartlett’s method (DiStefano, Zhu, & Mindrila, 2009). Two hierarchical regression approaches were taken, to ensure differences observed in main outcome variables were related to variables of interest, rather than potential confounding variables of baseline employment, negative symptoms, premorbid IQ and treatment group (Allott et al., 2011). The first regression model tested which cognitive domains would best predict employment duration in longest held job, while controlling for potentially confounding variables. Any cognitive domains and confounding variables significant in this regression were further assessed in the following regression models. The subsequent regression models tested whether the “fit” between a cognitive domain and job complexity predicted employment duration in the longest held job. This was tested using a stepwise model (Frazier, Tix, & Barron, 2004), with job complexity being proposed as the moderator between a cognitive domain and employment duration. Moderation is represented by an interaction between a cognitive domain and job complexity. In order for the variables to be entered into an interaction, they were first standardized into z scores, and then multiplied (Frazier et al., 2004). In the first step of the model, covariates were entered, in the second step the variables contained in the interaction term were entered (cognition and job complexity), in the third step the interaction was entered (Cognition ⫻ Job complexity), and the fourth step included interactions between the covariates and all other variables. The fourth step was performed to test if covariates acted consistently across levels of other variables, and are removed from the final model if the step change is not significant (Frazier et al., 2004). A significant moderation effect is indicated by a significant change in variability (R⌬2 ) explained when the interaction term is added (Frazier et al., 2004).
Results Sample Characteristics The 135 participants were representative of Australian FEP samples, with a mean age of early twenties, the majority being male, never married, unemployed, with relatively low educational attainment (see Table 1).
Statistical Analysis
Factor Analysis of Cognition
Statistical analysis was conducted using IBM SPSS Statistics Version 21.0.0. Exploratory factor analysis was used to identify clinically meaningful cognitive domains based on the neuropsychological test battery (Allott et al., 2011). The factorability of the correlation matrix of the neuropsychological test variables was ascertained by examining Kaiser’s measure of sampling adequacy (criterion ⬎0.6), Bartlett’s test of sphericity, and the diagonal of the anti-image correlation matrix (criterion r ⬎ .70). To determine the number of factors, the minimum average partial correlation test (Velicer, 1976) and the Parallel tests were run, as well as examination of the Scree plot. In addition to the chi-square test-of-fit, the root mean square error of approximation (RMSEA) was also calculated (criterion ⬍.05; Browne & Cudeck, 1993). Promax rotation was used, as oblique rotated solutions are strongly recom-
The TMT A, TMT B, and LCT data were substantially positively skewed and appropriate transformations were made (Tabachnick & Fidell, 2012). In support of the factorability of the data, Kaiser’s measure of sampling adequacy was 0.84. Bartlett’s test was significant, 2(78) ⫽ 867.59, p ⬍ .001. All values on the diagonal of the antimage matrix exceeded .70. Both the parallel test and the minimum average partial correlation test were indicative of a three-factor solution. However, when a three-factor solution was run, the Rey Auditory Verbal Memory Test total had a factor loading of 0.99, indicating a Heywood case, with no unique variance (Heywood, 1931). In line with recommendations, the solution was discarded, and different numbers of factors were extracted (Heywood, 1931). Based on the examination of the Scree plot and clinical meaningfulness, four factors were extracted. LCT
COGNITION AND EMPLOYMENT DURATION IN FEP
treatment allocation was significantly associated with gaining employment, with the majority of those who received IPS gaining employment, and the majority of those receiving TAU failing to gain employment (see Table 2).
Table 1 Baseline Demographics of Participants With First-Episode Psychosis Total sample (N ⫽ 135)
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Baseline variables Demographics Gender % Male Age (years) Marital status Never married Country of birth Australia Premorbid IQ WRAT scaled score Psychosis Duration of illness (months) Time since diagnosis (months) SANS composite Employment Age at first job (years) Currently in paid work Months since last in paid work Education Current study status Not studying Part-time Full-time Highest year completed at school Years 7–9 Year 10 Year 11 Year 12 or VCAL
213
% (n) M (SD)
67.41 (91) 20.35 (2.41)
% (n)
97.04 (131)
% (n)
74.81 (101)
Job Complexity Job complexity was determined for 64 participants whose job details were available. Job complexity ratings had a restricted range, with 17.19% of the sample being employed in Complexity 1 jobs, 43.75% in Complexity 2 jobs, 37.50% in Complexity 3 jobs, 1.56% in Complexity 4 jobs, and 0% in a Complexity 5 job.
M (SD)
92.13 (13.58)
Prediction of Outcome
M (SD) M (SD) M (SD)
16.95 (17.71) 9.00 (7.44) 25.71 (12.55)
M (SD) % (n) M (SD)
15.78 (2.13) 17.78 (24) 13.41 (16.30)
Cognition predicting employment duration in longest held job. The first regression model (see Table 3) accounted for a significant amount of variance in employment duration in longest 2 held job, F(8, 54) ⫽ 3.84, p ⫽ .001; R2 ⫽ .36, RAdjusted ⫽ .27. Neither baseline employment (p ⫽ .36), negative symptoms (p ⫽ .61), or premorbid IQ (p ⫽ .28) contributed significantly to the prediction of employment duration in longest held job and were therefore not included in the subsequent regression models. However, treatment (receiving IPS) was predictive of employment duration in longest held job (p ⫽ .03) and was retained in the subsequent models. Both VO&M, b ⫽ 4.45, t(54) ⫽ 4.30, p ⬍ .001, and VC&F, b ⫽ ⫺2.69, t(54) ⫽ ⫺2.24, p ⫽ .03, significantly predicted employment duration in longest held job. Therefore, VO&M and VC&F were subsequently examined in separate regression models. Job complexity moderating the relationship between cognition and employment duration in longest held job. A hierarchical regression model was used to determine if a fit between VO&M and job complexity predicted employment duration in longest held job. Treatment was retained in the final model. Step 4 did not significantly change the model and was therefore excluded from the final model (Frazier et al., 2004). Thus, the final model comprised three steps (see Table 4), accounting for a significant amount of variance in employment duration in longest 2 held job, F(4, 56) ⫽ 7.38, p ⬍ .001; R2 ⫽ .35, RAdjusted ⫽ .30. The model did not significantly change in Step 3 when the interaction term was entered, ⌬R2 ⫽ .01; ⌬F(1, 56) ⫽ 0.61, p ⫽ .44, meaning VO&M did not significantly moderate the effect of job complexity (Frazier et al., 2004). Furthermore, as independent variables, both VO&M and job complexity significantly predicted employment duration in longest held job. A further hierarchical regression model was used to determine if a fit between VC&F and job complexity predicted employment duration in longest held job. Treatment was again retained in the final model. Step 4 did not significantly change the model and was therefore excluded from the final model (Frazier et al., 2004). Thus, the final model comprised three steps (see Table 5), accounting for a significant amount of variance in employment duration in 2 longest held job, F(4, 58) ⫽ 3.91, p ⫽ .007; R2 ⫽ .21, RAdjusted ⫽ .16. The model did not significantly change in Step 3 when the interaction term was entered, ⌬R2 ⫽ .05; ⌬F(1, 58) ⫽ 03.31, p ⫽ .07, meaning VC&F did not significantly moderate the effect of job complexity (Frazier et al., 2004). Furthermore, as independent variables, both VC&F and job complexity did not predict employment duration in longest held job.
% (n) % (n) % (n)
80.74 (109) 8.89 (12) 10.37 (14)
% (n) % (n) % (n) % (n)
22.22 (30) 17.78 (24) 17.78 (24) 42.22 (57)
Note. M ⫽ mean; SD ⫽ standard deviation; n ⫽ number of participants; SANS ⫽ Scale for Assessment of Negative Symptoms; WRAT ⫽ Wide Range Achievement Test; VCAL ⫽ Victorian Certificate of Applied Learning.
(score and time), Picture Completion, Digit Span, and Animal Fluency were excluded because they had a primary factor loading ⬍0.40 and cross loadings ⬎0.30 (Tabachnick & Fidell, 2012). COWAT could have also been excluded from the factor analysis according to these criteria. However, removing COWAT resulted in a poor fitting factor solution, therefore it was retained. Based on the remaining 13 variables, the four factors were labeled as (a) attention and processing speed, (b) verbal learning and memory, (c) verbal comprehension and fluency (VC&F), and (d) visual organization and memory (VO&M); see online Supplementary Table. The model was found to be a close fit with the data: 2(32) ⫽ 32.00, p ⫽ .47 and RMSEA of .001 (Browne & Cudeck, 1993).
Employment Over 6 Months At 6-month follow-up, 115 participants were included in the sample. Over 6 months, 65 (56.5%) were employed and 50 (43.5%) were not. Information on employment duration was missing for two employed participants. Therefore, 63 participants were included in the regression analyses. The mean hours of employment in the longest held job over the 6-month follow-up for those employed was 395.45 (SD ⫽ 328.82). Those who worked during the study period were more likely to have been employed at baseline and to experience a lower level of negative symptoms, compared to those who did not work (see Table 2). Furthermore,
CARUANA, COTTON, KILLACKEY, AND ALLOTT
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Table 2 Baseline Demographics of Participants and Allocated Treatment Condition, Comparing Those Who Did and Did Not Work Over the 6-Month Study Period Employment status over 6-month study period Employed (n ⫽ 65)
Not employed (n ⫽ 50)
70.77 (46) 20.02 (2.60)
60.00 (30) 20.50 (2.00)
.16 .28
% (n)
98.46 (64)
96.00 (48)
.40
% (n)
73.85 (48)
78.00 (39)
.67
M (SD)
93.91 (13.86)
91.60 (13.30)
.37
M (SD) M (SD) M (SD)
17.97 (18.37) 10.22 (8.82) 23.32 (12.80)
14.84 (14.62) 7.20 (5.69) 29.94 (11.83)
.35 .06 .005ⴱⴱ
M (SD) % (n) M (SD)
15.70 (2.29) 30.77 (20) 3.21 (3.15)
15.53 (1.93) 2.00 (1) 4.48 (3.64)
.70 ⬍.001ⴱⴱⴱ .09
% (n) % (n) % (n)
76.93 (50) 7.69 (5) 15.38 (10)
88.00 (44) 10.00 (5) 2.00 (1)
% (n) % (n) % (n) % (n)
20.00 (13) 24.61 (16) 13.85 (9) 41.54 (27)
20.00 (10) 12.00 (6) 20.00 (10) 48.00 (24)
% (n) % (n)
63.08 (41) 36.92 (24)
42.00 (21) 58.00 (29)
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Baseline variables Demographics Gender % Male Age in years Marital status Never married Country of birth Australia Premorbid IQ WRAT scaled score Psychosis Duration of illness (months) Time since diagnosis (months) SANS composite Employment Age at first job Currently in paid work Months since last in paid work Education Current education status Not studying Studying part-time Studying full-time Highest year completed at school Years 7–9 Year 10 Year 11 Year 12 or VCAL Treatment allocation IPS plus TAU TAU alone
% (n) M (SD)
Sig.
.053
.35
.03ⴱ
Note. M ⫽ mean; SD ⫽ standard deviation; Sig. ⫽ significance; SANS ⫽ Scale for Assessment of Negative Symptoms; WRAT ⫽ Wide Range Achievement Test; VCAL ⫽ Victorian Certificate of Applied Learning; IPS ⫽ Individual Placement and Support; TAU ⫽ treatment as usual. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
Discussion Cognition and Employment Duration in Longest Held Job VO&M and VC&F were the two cognitive factors to significantly predict employment duration in the longest held job over 6 months, with better VO&M and poorer VC&F predicting longer employment duration. The finding in relation to VO&M is consistent with our previous report on all 135 participants in the same study (Allott et al., 2013) and consistent with studies in chronic schizophrenia showing the important role of cognition in predicting employment duration in particular, relative to employment attainment (Gold et al., 2002; McGurk et al., 2003). Few FEP studies have examined the relationship between cognition and employment duration, with previous findings in FEP finding a relationship between cognition and employment attainment (Dickerson et al., 2008; Nuechterlein et al., 2011; Tandberg et al., 2011). The finding of poorer VC&F predicting longer employment duration in the initial regression model is contrary to expectation, although this was no longer significant in the final model. Nevertheless, together, the findings strengthening the importance of assessing and targeting cognition as part of vocational rehabilitation programs.
Job Complexity and Cognition Predicting Employment Duration in Longest Held Job Against our hypothesis, job complexity did not moderate the relationship between VO&M and employment duration, suggesting a fit between cognition and job complexity is not related to employment duration in the longest held job. As an individual variable, job complexity did predict employment duration, with higher job complexity being associated with greater hours of employment in the longest held job. It may be speculated that higher job complexity predicting greater hours of employment is associated with greater job satisfaction and thus, tenure, although this hypothesis requires further investigation. VO&M also remained a significant predictor of employment duration in longest held job in the final regression model. This supports the notion that higher-order cognitive functions are vital for maintaining adequate performance in a work role and is highly consistent with a large body of research highlighting the importance of organization and memory in work outcomes, including duration (Gold et al., 2002; McGurk & Mueser, 2004; Velligan, Bow-Thomas, Mahurin, Miller, & Halgunseth, 2000). These findings suggest that both VO&M and job complexity are independently important for maintaining employment in young people with FEP. Thus, job com-
COGNITION AND EMPLOYMENT DURATION IN FEP
Table 3 Multiple Regression Model Examining Predictors of Employment Duration in Longest Held Job Over 6 Months Employment duration in longest held job
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Variables Constant Control variables Employed at baseline (0 ⫽ no, 1 ⫽ yes) Treatment (0 ⫽ TAU, 1 ⫽ IPS) WRAT scaled score SANS composite Predictor variables Attention and processing speed Verbal learning and memory VC&F VO&M
b
SE b
Sig.
9.73
9.23
.30
⫺2.50 ⫺4.57 0.11 0.05
2.70 2.02 0.10 0.09
.36 .03ⴱ .28 .61
⫺0.53 ⫺0.23 ⫺2.69 4.45
1.19 1.21 1.20 1.03
.66 .85 .03ⴱ ⬍.001ⴱⴱⴱ
Note. n ⫽ 63; SE b ⫽ standard error of b; Sig. ⫽ significance; TAU ⫽ treatment as usual; IPS ⫽ Individual Placement and Support; WRAT ⫽ Wide Range Achievement Test; SANS ⫽ Scale for Assessment of Negative Symptoms; VC&F ⫽ verbal comprehension and fluency; VO&M ⫽ visual organization and memory. ⴱ p ⬍ .05. ⴱⴱⴱ p ⬍ .001.
plexity as well as VO&M influences how long someone holds a job for, regardless of job complexity fit.
Limitations The amount of time taken to gain employment was not taken into account. This was because we were not able to accurately measure how many hours were taken to gain employment, proportionate to the hours worked in the longest held job. This is problematic as it means employment duration in longest held job has been influenced by the time taken to find employment, in addition to the actual duration of the job. Bond et al. (2008) recommended that the initial period of unemployment during job searching should be taken into account when investigating job duration.
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Second, there was a restricted range of job complexity ratings. The Social Adjustment Scale-Work Outcome may not be an appropriate scale for young FEP participants. Given the participants were in adolescence and emerging adulthood with relatively low educational attainment, it was not surprising that advanced skilled and professional occupation jobs were rarely gained. Therefore, further research is needed to develop a more age-appropriate and refined scale. Third, the results may have been impacted by participants’ education commitments, but this was not accounted for in the current study. This study included some participants who were studying, but working part time. It is possible that school commitments may have influenced job duration. For example, school commitments may have limited hours worked, or those working may have decided to stop working to return to education. Further research would benefit from taking into account education commitments, when investigating employment outcomes in adolescents and young adults, due to the normative significance of education during this developmental period. Finally, the neuropsychological tasks may have differed in their sensitivity, such that task performance differences could reflect differences in nonspecific effects of task difficulty or other psychometric characteristics, as opposed the differences in performance on the cognitive domain per se.
Conclusions and Implications For young people with FEP, vocational rehabilitation is often a main therapeutic goal (Iyer et al., 2011). VO&M was a significant predictor of employment duration for this FEP sample; however, the VO&M–job complexity fit was not important. This suggests that when considering employment duration, it is important to address VO&M but it is not necessary to ensure VO&M ability matches job complexity. This may be achieved through the addition of remediation- and compensatory-based cognitive rehabilitation approaches (e.g., McGurk et al., 2005). However, there is a lack of comparative studies in this area, particularly regarding job complexity and employment duration, and further research is needed to replicate these findings, while also taking into account educational commit-
Table 4 Hierarchical Regression Model Examining VO&M–Job Complexity Interaction Predicting Employment Duration in Longest Held Job Over 6-Month Study Period Employment duration in longest held job Variables Step 1 Control variable Treatment (0 ⫽ TAU, 1 ⫽ IPS) Step 2 Predictor variables VO&M Job complexity Step 3 Interaction effect VO&M ⫻ Job Complexity Total R2
b
⫺6.34 3.66 2.34 1.06
SE b
2.00 0.99 1.03 1.36
Sig.
⌬R2
Sig. ⌬R2
.10
.01ⴱ
.24
⬍.001ⴱⴱ
.007
.44
.002ⴱⴱ ⬍.001ⴱⴱⴱ .03ⴱ .44
.35ⴱⴱⴱ
Note. n ⫽ 63; VO&M ⫽ visual organization and memory; SE b ⫽ standard error of b; ⌬R2 ⫽ R2 change when step entered; Sig. ⫽ significance; TAU ⫽ treatment as usual; IPS ⫽ Individual Placement and Support. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
CARUANA, COTTON, KILLACKEY, AND ALLOTT
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Table 5 Hierarchical Regression Model Examining VC&F–Job Complexity Interaction Predicting Employment Duration in Longest Held Job Over 6-Month Study Period Employment duration in longest held job
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Variables Step 1 Control variable Treatment (0 ⫽ TAU, 1 ⫽ IPS) Step 2 Predictor variables VC&F Job complexity Step 3 Interaction effect VC&F ⫻ Job Complexity Total R2
b
⫺6.45 ⫺0.76 1.54 1.83
SE b
2.13 ⫺0.09 0.17 1.00
Sig.
⌬R2
Sig. ⌬R2
.10
.01ⴱ
.07
.09
.05
.07
.004ⴱⴱ .47 .18 .07
.21ⴱⴱ
Note. n ⫽ 63; VC&F ⫽ verbal comprehension and fluency; SE b ⫽ standard error of b; ⌬R2 ⫽ R2 change when step entered; Sig. ⫽ significance; TAU ⫽ treatment as usual; IPS ⫽ Individual Placement and Support. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.
ments. Furthermore, a job complexity scale appropriate for young FEP clients needs to be established. Despite a VO&M–job complexity fit not seeming to be important, other aspects of the person– organization fit perspective may still be useful to optimize vocational outcomes (Henry, 2004). Possibly, it is important that young people with FEP are working in a job they enjoy and that suits their personality, within an environment of compatible peers with desired levels of social interaction. Furthermore, it is possible that it is important for a job to match a person’s overall cognitive profile, rather than specific aspects of cognition. Future research should continue to assess the utility of adopting a personorganization fit perspective to promote positive vocational outcomes.
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One-year results from a randomized controlled trial. Schizophrenia Bulletin, 31, 898 –909. http://dx.doi.org/10.1093/schbul/sbi037 Nuechterlein, K. H., Subotnik, K. L., Green, M. F., Ventura, J., Asarnow, R. F., Gitlin, M. J., . . . Mintz, J. (2011). Neurocognitive predictors of work outcome in recent-onset schizophrenia. Schizophrenia Bulletin, 37(Suppl 2), S33–S40. http://dx.doi.org/10.1093/schbul/sbr084 Nuechterlein, K. H., Subotnik, K. L., Turner, L. R., Ventura, J., Becker, D. R., & Drake, R. E. (2008). Individual placement and support for individuals with recent-onset schizophrenia: Integrating supported education and supported employment. Psychiatric Rehabilitation Journal, 31, 340 –349. http://dx.doi.org/10.2975/31.4.2008.340.349 Osterrieth, P. A. (1944). Le test de copie d’une figure complex: Contribution a l’etude de la perception et de la memoire. Archives de Psychologie, 30, 286 –356. Rapp, C. A. (1998). The Strengths Model: Case management with people suffering from severe and persistent mental illness. New York, NY: Oxford University Press. Reitan, R. M. (1955). The relation of the trail making test to organic brain damage. Journal of Consulting Psychology, 19, 393–394. http://dx.doi .org/10.1037/h0044509 Rey, A. (1941). L’examen psychologique dans les cas d’encephalopathie traumatique. Archives de Psychologie, 28, 286 –340. Schmidt, M. (1996). Rey Auditory Verbal Learning Test. A handbook. Los Angeles, CA: Western Psychological Services. Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304 –321. http://dx.doi.org/10.1177/0734282911406653 Smith, A. (1982). Symbol Digit Modalities Test (SDMT). Manual (revised). Los Angeles, CA: Western Psychological Services. Strauss, E. H., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary (3rd ed.). New York, NY: Oxford University Press. Tabachnick, B. G., & Fidell, L. (2012). Using multivariate statistics (International ed.). Cranbury, NJ: Pearson. Tandberg, M., Ueland, T., Sundet, K., Haahr, U., Joa, I., Johannessen, J. O., . . . McGlashan, T. (2011). Neurocognition and occupational functioning in patients with first-episode psychosis: A 2-year follow-up study. Psychiatry Research, 188, 334 –342. http://dx.doi.org/10.1016/j .psychres.2011.04.021 The Psychological Corporation. (1997). WAIS-III and WMS-III technical manual. San Antonio, TX: The Psychological Corporation. Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327. http://dx.doi .org/10.1007/BF02293557 Velligan, D. I., Bow-Thomas, C. C., Mahurin, R. K., Miller, A. L., & Halgunseth, L. C. (2000). Do specific neurocognitive deficits predict specific domains of community function in schizophrenia? Journal of Nervous and Mental Disease, 188, 518 –524. http://dx.doi.org/10.1097/ 00005053-200008000-00007 Verdoux, H., Liraud, F., Assens, F., Abalan, F., & van Os, J. (2002). Social and clinical consequences of cognitive deficits in early psychosis: A two-year follow-up study of first-admitted patients. Schizophrenia Research, 56(1–2), 14, 9 –159. http://dx.doi.org/10.1016/S09209964(01)00225-0 Weissman, M. M., & Paykel, E. S. (1974). The depressed woman: A study of social relationships. Chicago, IL: University of Chicago Press. Wilkinson, G. S., & Robertson, G. J. (2006). WRAT-4: Wide Range Achievement Test. Professional manual. Lutz, FL: Psychological Assessment Resources, Inc.
Received April 8, 2014 Revision received January 14, 2015 Accepted January 21, 2015 䡲
© 2015 American Psychological Association 1095-158X/15/$12.00 http://dx.doi.org/10.1037/prj0000129
The Relationship Between Cognition, Job Complexity, and Employment Duration in First-Episode Psychosis Emma Caruana
Susan Cotton and Eóin Killackey
The University of Melbourne
Orygen, The National Centre of Excellence in Youth Mental Health, Parkville Victoria, Australia, and The University of Melbourne
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Kelly Allott Orygen, The National Centre of Excellence in Youth Mental Health, Parkville Victoria, Australia, and University of Melbourne Objective: To investigate the relationship between cognition and employment duration in firstepisode psychosis (FEP), and establish if a “fit” between cognition and job complexity is associated with longer employment duration. Method: This study involved secondary data analysis of a subsample of FEP individuals (n ⫽ 65) who participated in a randomized controlled trial comparing Individual Placement and Support plus treatment as usual (TAU), versus TAU alone, over 6 months. A cognitive battery was administered at baseline and employment duration (hours) and job complexity in the longest held job over 6 months were measured. Results: Factor analysis with promax rotation of the cognitive battery revealed 4 cognitive domains: (a) attention and processing speed; (b) verbal learning and memory; (c) verbal comprehension and fluency; and (d) visual organization and memory (VO&M). The final hierarchical regression model found that VO&M and job complexity independently predicted employment duration in longest held job; however, the “fit” (or interaction) between VO&M and job complexity was not significant. Conclusions and Implications for Practice: These findings suggest that VO&M and job complexity are important predictors of employment duration, but it is not necessary to ensure VO&M ability matches job complexity. However, there are limited comparative studies in this area, and other aspects of the person– organization fit perspective may still be useful to optimize vocational outcomes in FEP. Keywords: cognition, job complexity, employment duration, supported employment, first-episode psychosis Supplemental materials: http://dx.doi.org/10.1037/prj0000129.supp
Supported employment (SE) is the leading evidence-based vocational intervention for severe mental disorders (Killackey, Jackson, Gleeson, Hickie, & McGorry, 2006). Individual Placement and Support (IPS) is a highly defined form of SE (Drake, Bond, & Becker, 2012) with effectiveness in improving employment outcomes in chronic psychotic disorders (e.g., Drake et al., 2012) and FEP (Killackey, Jackson, & McGorry, 2008; Nuechterlein et al., 2008). We recently completed a randomized controlled trial involving 146 FEP participants and found that 6 months of IPS resulted in significantly higher rates of employment and education (88.1%), compared to treatment as usual (TAU; 72.1%; p ⫽ .023; Allott et al., 2013; Killackey et al., 2012). However, there was no difference between IPS and TAU in employment duration (mean hours worked) over the 6-month period (247.2 vs. 241.5; p ⫽ .927; Allott et al., 2013). Further, in chronic schizophrenia, over 50% of individuals experience unsatisfactory job terminations (Bond, Drake, & Becker, 2008), and have difficulties maintaining employment after SE (McGurk & Meltzer, 2000). These findings raise issues about how to optimize vocational outcomes and call for a better understanding of enhancing employment duration in psychosis.
Unemployment is a significant problem for individuals with first-episode psychosis (FEP). Approximately half of young people are unemployed by the time they seek help (Fraser, Berger, Killackey, & McGorry, 2006) and express vocational rehabilitation as a major treatment goal (Iyer, Mangala, Anitha, Thara, & Malla, 2011). Employment rates decline further in the year following admission for FEP (Marwaha & Johnson, 2004). Unsurprisingly, clinical guidelines for FEP strongly recommend early vocational rehabilitation (Early Psychosis Guidelines Writing Group, 2010).
This article was published Online First April 6, 2015. Emma Caruana, Melbourne School of Psychological Sciences, The University of Melbourne; Susan Cotton and Eóin Killackey, Orygen, The National Centre of Excellence in Youth Mental Health, Parkville Victoria, Australia, and Centre for Youth Mental Health, The University of Melbourne; Kelly Allott, DPsych, Orygen, The National Centre of Excellence in Youth Mental Health, and Centre for Youth Mental Health, The University of Melbourne. Correspondence concerning this article should be addressed to Kelly Allott, 35 Poplar Road, Parkville Victoria, Australia 3052. E-mail: [email protected] 210
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COGNITION AND EMPLOYMENT DURATION IN FEP
Cognition is a key predictor of vocational outcomes in established schizophrenia generally (Bryson & Bell, 2003; McGurk & Mueser, 2004), and within the context of vocational rehabilitation (Lysaker, Bell, & Beam-Goulet, 1995; Lysaker, Bryson, Davis, & Bell, 2005; McGurk & Mueser, 2004; McGurk, Mueser, Harvey, LaPuglia, & Marder, 2003). However, studies in FEP have reported mixed findings (Allott, Liu, Proffitt, & Killackey, 2011), with several studies finding a significant relationship between cognition and vocational functioning (Dickerson et al., 2008; Nuechterlein et al., 2011; Tandberg et al., 2011), and others reporting no relationship (Verdoux, Liraud, Assens, Abalan, & van Os, 2002). Allott et al. (2011) identified that these mixed findings may be due to lack of control for additional predictor variables, such as employment history, general intellectual functioning (IQ) and negative symptoms, and recommended future studies control for these. A further methodological issue which may explain the variation found across studies is the large variation in measurement of cognition (Allott et al., 2011). The use of multiple neuropsychological tests that measure functioning across several cognitive domains, and the employment of data reduction techniques to validate which cognitive domains each test represents, may improve the reliability of findings in relation to cognition and vocational outcomes (Bowie et al., 2008; Gladsjo et al., 2004; Jaeger, Czobor, & Berns, 2003). The use of data reduction techniques is also important given the multifactorial nature of some of these tests; with some tests measuring multiple cognitive domains, rather than mapping neatly onto one domain (Kaplan, 1990). In addition to methodological issues, none of these studies investigated the relationship between cognition and employment duration in FEP. The predictors of employment attainment may differ from the predictors of employment tenure (Gold, Goldberg, McNary, Dixon, & Lehman, 2002; McGurk et al., 2003). We recently found that within the context of IPS, although cognition did not predict whether participants gained employment, higher visual organization and memory predicted greater employment duration (hours) over 6 months (Allott et al., 2013). This leads to the question of whether job tenure may be improved if the cognitive ability of an individual is matched with the cognitive complexity of their job. Remediating or accommodating cognitive deficits may be necessary to improve vocational outcomes such as employment duration (Allott et al., 2013; McGurk & Mueser, 2003). Cognitive remediation within a supported employment program has shown promise in further improving vocational outcomes (McGurk, Mueser, Feldman, Wolfe, & Pascaris, 2007; McGurk, Mueser, & Pascaris, 2005). With regard to accommodating cognitive deficits, McGurk and Mueser (2003) proposed two models instrumental to the success of SE: the compensatory model and the environmental niche model. The compensatory model hypothesizes that SE is effective when it compensates for the cognitive and symptom impairments associated with psychotic disorders that would otherwise interfere with work (e.g., minimizing cognitive fatigue by scheduling regular work breaks). Conversely, the environmental niche draws on the strengths model of case management (Rapp, 1998). Here, SE is effective when it helps clients find work in settings where their cognitive impairments and symptoms pose minimal interference, and where natural supports (e.g., coworkers, family, friends) may be harnessed to minimize their negative
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effects on work (e.g., matching job complexity with an individual’s cognitive ability). Job complexity is the cognitive complexity of job activities, such as whether a job has higher (e.g., office manager) or lower (e.g., typist) cognitive demands. Employment success for individuals with severe mental illness may be assisted through adopting a person– organization fit perspective, but this has yet to be explored (Henry, 2004). In chronic schizophrenia, executive functioning is significantly associated with job complexity within the context of independent employment, but not SE (McGurk & Mueser, 2003). Those independently employed and with lower executive functioning had less cognitively complex jobs; they had found a natural environmental niche where deficits were accommodated. The lack of association between executive functioning and job complexity in SE suggests that SE was compensating for deficits, rather than matching job complexity to one’s cognitive ability. Thus, matching job complexity and executive functioning may be a prerequisite for clients to move from SE to independent employment (McGurk and Mueser (2003). However, other cognitive deficits may also be important, and a job may have to match an individual’s overall cognitive profile in order for clients to maintain employment as SE support diminishes. Overall, a cognition-job complexity fit may also be important for employment duration, but this has not been previously investigated. The aim of the present study was to investigate the role of cognition in employment duration in young people with FEP, and the potential moderating role of job complexity. We first hypothesized that while controlling for baseline employment, negative symptoms, and premorbid IQ, cognitive domains would predict employment duration in the longest held job over 6 months. Second, we hypothesized that while controlling for the same factors, the “fit” between cognitive domains and job complexity would be associated with improved employment duration in the longest held job over 6 months.
Method Study Design and Participants This study involved secondary data analysis of a randomized controlled trial that was conducted with clients of the Early Psychosis Prevention and Intervention Centre in Melbourne, Australia (see Killackey et al., 2013, for full description of trial). Participants either received IPS plus TAU, or TAU alone, over a 6-month period. Using the Supported Employment Fidelity Scale (Becker, Swanson, Bond, & Merrens, 2011), the IPS delivered in the trial was rated as having “good” fidelity to the model. Inclusion criteria were aged between 15 and 25 years (M ⫽ 20.35, SD ⫽ 2.41), a first episode of a Diagnostic and Statistical Manual-IV-TR-defined psychotic disorder, and an expressed desire to pursue an employment or educational goal. Exclusion criteria included intellectual disability, florid psychosis preventing informed consent, and insufficient English to complete assessments. Of the 146 eligible participants, 11 with premorbid neurological conditions were excluded, leaving 135 participants. These participants were included in the factor analysis described below. The primary analysis was then conducted only on those participants who had worked during the study period. Participants were as-
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CARUANA, COTTON, KILLACKEY, AND ALLOTT
sessed by a research assistant blind to treatment allocation at baseline and 6 months (postintervention).
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Measures Baseline. Baseline assessment covered demographics, including age, gender, marital status, duration of illness, time since diagnosis, educational achievement, employment status (currently in paid employment–yes/no), employment history (months in paid work), and treatment allocation (IPS plus TAU/TAU). Given the literature showing evidence for a relationship between negative symptoms and cognition, the Scale for Assessment of Negative Symptoms composite score (Andreasen, 1984) was included in the current analyses. For the same reason, an estimate of premorbid intellectual functioning was measured using the Wide Range Achievement Test– 4th Edition—reading subtest scaled score (Wilkinson & Robertson, 2006). A neuropsychological battery comprising 11 tests was administered. Subtests of the Wechsler Adult Intelligence Scale—Third Edition (The Psychological Corporation, 1997) were administered, including Similarities, Information, Digit Span, Letter-Number Sequencing and Picture Completion. Other tests were Trail Making Test A and B (TMT; Reitan, 1955), Symbol-Digit Modalities Test (Smith, 1982), Letter Cancellation Task (LCT; Strauss, Sherman, & Spreen, 2006), Rey Auditory Verbal Memory Test (Schmidt, 1996), Rey-Osterrieth Complex Figure Test (Osterrieth, 1944; Rey, 1941), Controlled Oral Word Association Test (COWAT) and the Animal Fluency Test (Strauss et al., 2006). Six-month follow-up. Employment duration in longest held job was measured as the total number of hours worked in the job held for the longest time over the 6-month follow-up period. Due to a moderate positively skewed distribution, this variable was square root transformed (Tabachnick & Fidell, 2012). Job complexity of longest held job was measured using the Social Adjustment Scale-Work Outcome (Weissman & Paykel, 1974). The cognitive complexity of a job is measured on a 5-point scale: (1) none-unskilled laborer; (2) nontechnical skilled work; (3) entry-level skilled work; (4) advanced skilled work; (5) very high-professional occupation. Two independent raters blind to treatment allocation and cognitive functioning rated the jobs gained by participants. When there was a discrepancy between ratings, consensus was obtained through joint discussion.
mended, resulting in more realistic and statistically sound factor structures (Schmitt, 2011). Factor scores were estimated using the recommended Bartlett’s method (DiStefano, Zhu, & Mindrila, 2009). Two hierarchical regression approaches were taken, to ensure differences observed in main outcome variables were related to variables of interest, rather than potential confounding variables of baseline employment, negative symptoms, premorbid IQ and treatment group (Allott et al., 2011). The first regression model tested which cognitive domains would best predict employment duration in longest held job, while controlling for potentially confounding variables. Any cognitive domains and confounding variables significant in this regression were further assessed in the following regression models. The subsequent regression models tested whether the “fit” between a cognitive domain and job complexity predicted employment duration in the longest held job. This was tested using a stepwise model (Frazier, Tix, & Barron, 2004), with job complexity being proposed as the moderator between a cognitive domain and employment duration. Moderation is represented by an interaction between a cognitive domain and job complexity. In order for the variables to be entered into an interaction, they were first standardized into z scores, and then multiplied (Frazier et al., 2004). In the first step of the model, covariates were entered, in the second step the variables contained in the interaction term were entered (cognition and job complexity), in the third step the interaction was entered (Cognition ⫻ Job complexity), and the fourth step included interactions between the covariates and all other variables. The fourth step was performed to test if covariates acted consistently across levels of other variables, and are removed from the final model if the step change is not significant (Frazier et al., 2004). A significant moderation effect is indicated by a significant change in variability (R⌬2 ) explained when the interaction term is added (Frazier et al., 2004).
Results Sample Characteristics The 135 participants were representative of Australian FEP samples, with a mean age of early twenties, the majority being male, never married, unemployed, with relatively low educational attainment (see Table 1).
Statistical Analysis
Factor Analysis of Cognition
Statistical analysis was conducted using IBM SPSS Statistics Version 21.0.0. Exploratory factor analysis was used to identify clinically meaningful cognitive domains based on the neuropsychological test battery (Allott et al., 2011). The factorability of the correlation matrix of the neuropsychological test variables was ascertained by examining Kaiser’s measure of sampling adequacy (criterion ⬎0.6), Bartlett’s test of sphericity, and the diagonal of the anti-image correlation matrix (criterion r ⬎ .70). To determine the number of factors, the minimum average partial correlation test (Velicer, 1976) and the Parallel tests were run, as well as examination of the Scree plot. In addition to the chi-square test-of-fit, the root mean square error of approximation (RMSEA) was also calculated (criterion ⬍.05; Browne & Cudeck, 1993). Promax rotation was used, as oblique rotated solutions are strongly recom-
The TMT A, TMT B, and LCT data were substantially positively skewed and appropriate transformations were made (Tabachnick & Fidell, 2012). In support of the factorability of the data, Kaiser’s measure of sampling adequacy was 0.84. Bartlett’s test was significant, 2(78) ⫽ 867.59, p ⬍ .001. All values on the diagonal of the antimage matrix exceeded .70. Both the parallel test and the minimum average partial correlation test were indicative of a three-factor solution. However, when a three-factor solution was run, the Rey Auditory Verbal Memory Test total had a factor loading of 0.99, indicating a Heywood case, with no unique variance (Heywood, 1931). In line with recommendations, the solution was discarded, and different numbers of factors were extracted (Heywood, 1931). Based on the examination of the Scree plot and clinical meaningfulness, four factors were extracted. LCT
COGNITION AND EMPLOYMENT DURATION IN FEP
treatment allocation was significantly associated with gaining employment, with the majority of those who received IPS gaining employment, and the majority of those receiving TAU failing to gain employment (see Table 2).
Table 1 Baseline Demographics of Participants With First-Episode Psychosis Total sample (N ⫽ 135)
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Baseline variables Demographics Gender % Male Age (years) Marital status Never married Country of birth Australia Premorbid IQ WRAT scaled score Psychosis Duration of illness (months) Time since diagnosis (months) SANS composite Employment Age at first job (years) Currently in paid work Months since last in paid work Education Current study status Not studying Part-time Full-time Highest year completed at school Years 7–9 Year 10 Year 11 Year 12 or VCAL
213
% (n) M (SD)
67.41 (91) 20.35 (2.41)
% (n)
97.04 (131)
% (n)
74.81 (101)
Job Complexity Job complexity was determined for 64 participants whose job details were available. Job complexity ratings had a restricted range, with 17.19% of the sample being employed in Complexity 1 jobs, 43.75% in Complexity 2 jobs, 37.50% in Complexity 3 jobs, 1.56% in Complexity 4 jobs, and 0% in a Complexity 5 job.
M (SD)
92.13 (13.58)
Prediction of Outcome
M (SD) M (SD) M (SD)
16.95 (17.71) 9.00 (7.44) 25.71 (12.55)
M (SD) % (n) M (SD)
15.78 (2.13) 17.78 (24) 13.41 (16.30)
Cognition predicting employment duration in longest held job. The first regression model (see Table 3) accounted for a significant amount of variance in employment duration in longest 2 held job, F(8, 54) ⫽ 3.84, p ⫽ .001; R2 ⫽ .36, RAdjusted ⫽ .27. Neither baseline employment (p ⫽ .36), negative symptoms (p ⫽ .61), or premorbid IQ (p ⫽ .28) contributed significantly to the prediction of employment duration in longest held job and were therefore not included in the subsequent regression models. However, treatment (receiving IPS) was predictive of employment duration in longest held job (p ⫽ .03) and was retained in the subsequent models. Both VO&M, b ⫽ 4.45, t(54) ⫽ 4.30, p ⬍ .001, and VC&F, b ⫽ ⫺2.69, t(54) ⫽ ⫺2.24, p ⫽ .03, significantly predicted employment duration in longest held job. Therefore, VO&M and VC&F were subsequently examined in separate regression models. Job complexity moderating the relationship between cognition and employment duration in longest held job. A hierarchical regression model was used to determine if a fit between VO&M and job complexity predicted employment duration in longest held job. Treatment was retained in the final model. Step 4 did not significantly change the model and was therefore excluded from the final model (Frazier et al., 2004). Thus, the final model comprised three steps (see Table 4), accounting for a significant amount of variance in employment duration in longest 2 held job, F(4, 56) ⫽ 7.38, p ⬍ .001; R2 ⫽ .35, RAdjusted ⫽ .30. The model did not significantly change in Step 3 when the interaction term was entered, ⌬R2 ⫽ .01; ⌬F(1, 56) ⫽ 0.61, p ⫽ .44, meaning VO&M did not significantly moderate the effect of job complexity (Frazier et al., 2004). Furthermore, as independent variables, both VO&M and job complexity significantly predicted employment duration in longest held job. A further hierarchical regression model was used to determine if a fit between VC&F and job complexity predicted employment duration in longest held job. Treatment was again retained in the final model. Step 4 did not significantly change the model and was therefore excluded from the final model (Frazier et al., 2004). Thus, the final model comprised three steps (see Table 5), accounting for a significant amount of variance in employment duration in 2 longest held job, F(4, 58) ⫽ 3.91, p ⫽ .007; R2 ⫽ .21, RAdjusted ⫽ .16. The model did not significantly change in Step 3 when the interaction term was entered, ⌬R2 ⫽ .05; ⌬F(1, 58) ⫽ 03.31, p ⫽ .07, meaning VC&F did not significantly moderate the effect of job complexity (Frazier et al., 2004). Furthermore, as independent variables, both VC&F and job complexity did not predict employment duration in longest held job.
% (n) % (n) % (n)
80.74 (109) 8.89 (12) 10.37 (14)
% (n) % (n) % (n) % (n)
22.22 (30) 17.78 (24) 17.78 (24) 42.22 (57)
Note. M ⫽ mean; SD ⫽ standard deviation; n ⫽ number of participants; SANS ⫽ Scale for Assessment of Negative Symptoms; WRAT ⫽ Wide Range Achievement Test; VCAL ⫽ Victorian Certificate of Applied Learning.
(score and time), Picture Completion, Digit Span, and Animal Fluency were excluded because they had a primary factor loading ⬍0.40 and cross loadings ⬎0.30 (Tabachnick & Fidell, 2012). COWAT could have also been excluded from the factor analysis according to these criteria. However, removing COWAT resulted in a poor fitting factor solution, therefore it was retained. Based on the remaining 13 variables, the four factors were labeled as (a) attention and processing speed, (b) verbal learning and memory, (c) verbal comprehension and fluency (VC&F), and (d) visual organization and memory (VO&M); see online Supplementary Table. The model was found to be a close fit with the data: 2(32) ⫽ 32.00, p ⫽ .47 and RMSEA of .001 (Browne & Cudeck, 1993).
Employment Over 6 Months At 6-month follow-up, 115 participants were included in the sample. Over 6 months, 65 (56.5%) were employed and 50 (43.5%) were not. Information on employment duration was missing for two employed participants. Therefore, 63 participants were included in the regression analyses. The mean hours of employment in the longest held job over the 6-month follow-up for those employed was 395.45 (SD ⫽ 328.82). Those who worked during the study period were more likely to have been employed at baseline and to experience a lower level of negative symptoms, compared to those who did not work (see Table 2). Furthermore,
CARUANA, COTTON, KILLACKEY, AND ALLOTT
214
Table 2 Baseline Demographics of Participants and Allocated Treatment Condition, Comparing Those Who Did and Did Not Work Over the 6-Month Study Period Employment status over 6-month study period Employed (n ⫽ 65)
Not employed (n ⫽ 50)
70.77 (46) 20.02 (2.60)
60.00 (30) 20.50 (2.00)
.16 .28
% (n)
98.46 (64)
96.00 (48)
.40
% (n)
73.85 (48)
78.00 (39)
.67
M (SD)
93.91 (13.86)
91.60 (13.30)
.37
M (SD) M (SD) M (SD)
17.97 (18.37) 10.22 (8.82) 23.32 (12.80)
14.84 (14.62) 7.20 (5.69) 29.94 (11.83)
.35 .06 .005ⴱⴱ
M (SD) % (n) M (SD)
15.70 (2.29) 30.77 (20) 3.21 (3.15)
15.53 (1.93) 2.00 (1) 4.48 (3.64)
.70 ⬍.001ⴱⴱⴱ .09
% (n) % (n) % (n)
76.93 (50) 7.69 (5) 15.38 (10)
88.00 (44) 10.00 (5) 2.00 (1)
% (n) % (n) % (n) % (n)
20.00 (13) 24.61 (16) 13.85 (9) 41.54 (27)
20.00 (10) 12.00 (6) 20.00 (10) 48.00 (24)
% (n) % (n)
63.08 (41) 36.92 (24)
42.00 (21) 58.00 (29)
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Baseline variables Demographics Gender % Male Age in years Marital status Never married Country of birth Australia Premorbid IQ WRAT scaled score Psychosis Duration of illness (months) Time since diagnosis (months) SANS composite Employment Age at first job Currently in paid work Months since last in paid work Education Current education status Not studying Studying part-time Studying full-time Highest year completed at school Years 7–9 Year 10 Year 11 Year 12 or VCAL Treatment allocation IPS plus TAU TAU alone
% (n) M (SD)
Sig.
.053
.35
.03ⴱ
Note. M ⫽ mean; SD ⫽ standard deviation; Sig. ⫽ significance; SANS ⫽ Scale for Assessment of Negative Symptoms; WRAT ⫽ Wide Range Achievement Test; VCAL ⫽ Victorian Certificate of Applied Learning; IPS ⫽ Individual Placement and Support; TAU ⫽ treatment as usual. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
Discussion Cognition and Employment Duration in Longest Held Job VO&M and VC&F were the two cognitive factors to significantly predict employment duration in the longest held job over 6 months, with better VO&M and poorer VC&F predicting longer employment duration. The finding in relation to VO&M is consistent with our previous report on all 135 participants in the same study (Allott et al., 2013) and consistent with studies in chronic schizophrenia showing the important role of cognition in predicting employment duration in particular, relative to employment attainment (Gold et al., 2002; McGurk et al., 2003). Few FEP studies have examined the relationship between cognition and employment duration, with previous findings in FEP finding a relationship between cognition and employment attainment (Dickerson et al., 2008; Nuechterlein et al., 2011; Tandberg et al., 2011). The finding of poorer VC&F predicting longer employment duration in the initial regression model is contrary to expectation, although this was no longer significant in the final model. Nevertheless, together, the findings strengthening the importance of assessing and targeting cognition as part of vocational rehabilitation programs.
Job Complexity and Cognition Predicting Employment Duration in Longest Held Job Against our hypothesis, job complexity did not moderate the relationship between VO&M and employment duration, suggesting a fit between cognition and job complexity is not related to employment duration in the longest held job. As an individual variable, job complexity did predict employment duration, with higher job complexity being associated with greater hours of employment in the longest held job. It may be speculated that higher job complexity predicting greater hours of employment is associated with greater job satisfaction and thus, tenure, although this hypothesis requires further investigation. VO&M also remained a significant predictor of employment duration in longest held job in the final regression model. This supports the notion that higher-order cognitive functions are vital for maintaining adequate performance in a work role and is highly consistent with a large body of research highlighting the importance of organization and memory in work outcomes, including duration (Gold et al., 2002; McGurk & Mueser, 2004; Velligan, Bow-Thomas, Mahurin, Miller, & Halgunseth, 2000). These findings suggest that both VO&M and job complexity are independently important for maintaining employment in young people with FEP. Thus, job com-
COGNITION AND EMPLOYMENT DURATION IN FEP
Table 3 Multiple Regression Model Examining Predictors of Employment Duration in Longest Held Job Over 6 Months Employment duration in longest held job
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Variables Constant Control variables Employed at baseline (0 ⫽ no, 1 ⫽ yes) Treatment (0 ⫽ TAU, 1 ⫽ IPS) WRAT scaled score SANS composite Predictor variables Attention and processing speed Verbal learning and memory VC&F VO&M
b
SE b
Sig.
9.73
9.23
.30
⫺2.50 ⫺4.57 0.11 0.05
2.70 2.02 0.10 0.09
.36 .03ⴱ .28 .61
⫺0.53 ⫺0.23 ⫺2.69 4.45
1.19 1.21 1.20 1.03
.66 .85 .03ⴱ ⬍.001ⴱⴱⴱ
Note. n ⫽ 63; SE b ⫽ standard error of b; Sig. ⫽ significance; TAU ⫽ treatment as usual; IPS ⫽ Individual Placement and Support; WRAT ⫽ Wide Range Achievement Test; SANS ⫽ Scale for Assessment of Negative Symptoms; VC&F ⫽ verbal comprehension and fluency; VO&M ⫽ visual organization and memory. ⴱ p ⬍ .05. ⴱⴱⴱ p ⬍ .001.
plexity as well as VO&M influences how long someone holds a job for, regardless of job complexity fit.
Limitations The amount of time taken to gain employment was not taken into account. This was because we were not able to accurately measure how many hours were taken to gain employment, proportionate to the hours worked in the longest held job. This is problematic as it means employment duration in longest held job has been influenced by the time taken to find employment, in addition to the actual duration of the job. Bond et al. (2008) recommended that the initial period of unemployment during job searching should be taken into account when investigating job duration.
215
Second, there was a restricted range of job complexity ratings. The Social Adjustment Scale-Work Outcome may not be an appropriate scale for young FEP participants. Given the participants were in adolescence and emerging adulthood with relatively low educational attainment, it was not surprising that advanced skilled and professional occupation jobs were rarely gained. Therefore, further research is needed to develop a more age-appropriate and refined scale. Third, the results may have been impacted by participants’ education commitments, but this was not accounted for in the current study. This study included some participants who were studying, but working part time. It is possible that school commitments may have influenced job duration. For example, school commitments may have limited hours worked, or those working may have decided to stop working to return to education. Further research would benefit from taking into account education commitments, when investigating employment outcomes in adolescents and young adults, due to the normative significance of education during this developmental period. Finally, the neuropsychological tasks may have differed in their sensitivity, such that task performance differences could reflect differences in nonspecific effects of task difficulty or other psychometric characteristics, as opposed the differences in performance on the cognitive domain per se.
Conclusions and Implications For young people with FEP, vocational rehabilitation is often a main therapeutic goal (Iyer et al., 2011). VO&M was a significant predictor of employment duration for this FEP sample; however, the VO&M–job complexity fit was not important. This suggests that when considering employment duration, it is important to address VO&M but it is not necessary to ensure VO&M ability matches job complexity. This may be achieved through the addition of remediation- and compensatory-based cognitive rehabilitation approaches (e.g., McGurk et al., 2005). However, there is a lack of comparative studies in this area, particularly regarding job complexity and employment duration, and further research is needed to replicate these findings, while also taking into account educational commit-
Table 4 Hierarchical Regression Model Examining VO&M–Job Complexity Interaction Predicting Employment Duration in Longest Held Job Over 6-Month Study Period Employment duration in longest held job Variables Step 1 Control variable Treatment (0 ⫽ TAU, 1 ⫽ IPS) Step 2 Predictor variables VO&M Job complexity Step 3 Interaction effect VO&M ⫻ Job Complexity Total R2
b
⫺6.34 3.66 2.34 1.06
SE b
2.00 0.99 1.03 1.36
Sig.
⌬R2
Sig. ⌬R2
.10
.01ⴱ
.24
⬍.001ⴱⴱ
.007
.44
.002ⴱⴱ ⬍.001ⴱⴱⴱ .03ⴱ .44
.35ⴱⴱⴱ
Note. n ⫽ 63; VO&M ⫽ visual organization and memory; SE b ⫽ standard error of b; ⌬R2 ⫽ R2 change when step entered; Sig. ⫽ significance; TAU ⫽ treatment as usual; IPS ⫽ Individual Placement and Support. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
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Table 5 Hierarchical Regression Model Examining VC&F–Job Complexity Interaction Predicting Employment Duration in Longest Held Job Over 6-Month Study Period Employment duration in longest held job
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Variables Step 1 Control variable Treatment (0 ⫽ TAU, 1 ⫽ IPS) Step 2 Predictor variables VC&F Job complexity Step 3 Interaction effect VC&F ⫻ Job Complexity Total R2
b
⫺6.45 ⫺0.76 1.54 1.83
SE b
2.13 ⫺0.09 0.17 1.00
Sig.
⌬R2
Sig. ⌬R2
.10
.01ⴱ
.07
.09
.05
.07
.004ⴱⴱ .47 .18 .07
.21ⴱⴱ
Note. n ⫽ 63; VC&F ⫽ verbal comprehension and fluency; SE b ⫽ standard error of b; ⌬R2 ⫽ R2 change when step entered; Sig. ⫽ significance; TAU ⫽ treatment as usual; IPS ⫽ Individual Placement and Support. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.
ments. Furthermore, a job complexity scale appropriate for young FEP clients needs to be established. Despite a VO&M–job complexity fit not seeming to be important, other aspects of the person– organization fit perspective may still be useful to optimize vocational outcomes (Henry, 2004). Possibly, it is important that young people with FEP are working in a job they enjoy and that suits their personality, within an environment of compatible peers with desired levels of social interaction. Furthermore, it is possible that it is important for a job to match a person’s overall cognitive profile, rather than specific aspects of cognition. Future research should continue to assess the utility of adopting a personorganization fit perspective to promote positive vocational outcomes.
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Received April 8, 2014 Revision received January 14, 2015 Accepted January 21, 2015 䡲
