Effect of a Vocationally‐Focused Brief Cognitive Behavioural Intervention on Employment‐Related Outcomes for Individuals with Mood and Anxiety Disorders
Bibliographic record
Abstract
Despite an increasing emphasis on the importance of vocational success to the quality of life of individuals with mental illness (Bond, Drake, & Becker, 2008 Bond, G. R., Drake, R. E. and Becker, R. B. 2008. An update on randomized controlled trials of evidence‐based supported employment.. Psychiatric Rehabilitation Journal, 31: 280–290. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), minimal work has examined the impact of cognitive behavioural interventions that focus on vocational stressors. Vocational stressors commonly faced by persons with mental illness include difficulties with work task completion, obtaining employment, and coping with interpersonal stressors (Becker et al., 1998 Becker, D. R., Drake, R. E., Bond, G. R., Xie, H., Dain, B. J. and Harrison, K. 1998. Job terminations among people with severe mental illness participating in supported employment.. Community Mental Health Journal, 34: 71–82. [Google Scholar]). The purpose of this pilot study was to examine the effectiveness of a brief cognitive behavioural therapy group intervention that targets vocational stressors for individuals whose vocational functioning had been significantly impacted by mental illness. Participants included 16 individuals with mood and anxiety disorder diagnoses. After this intervention, it was found that employed persons reported an improved sense of mastery in the completion of work tasks, improved satisfaction with work supervision, and decreased satisfaction with advancement and job security. Unemployed participants reported improved expectancy for employment success.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".