Predictors of job tenure for people with a severe mental illness, enrolled in supported employment programs.
Bibliographic record
Abstract
OBJECTIVE: Different predictors of job tenure for people with a severe mental illness (SMI) have been documented. Conflicting results may be explained by the choice of indicators to measure job tenure. This study aimed to assess the contribution of employment specialist competencies working in supported employment programs, client variables, and work accommodations, in determining job tenure in the regular labor market. METHOD: = 209) registered in 24 Canadian supported employment programs. Multivariable modeling analyses were performed. RESULTS: = 140) of the sample were employed at the 6-month follow-up. Multilevel analyses showed that shorter duration of unemployment (i.e., the number of weeks worked), employment specialist knowledge, and working alliance were the strongest predictors of job tenure for people with SMI. With respect to the number of hours worked per week, diagnosis, executive functions, social functioning, work accommodations, and employment specialist skills were the strongest predictors of job tenure for people with SMI, with 57% of variance explained. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Understanding the various predictors of job tenure can assist employment specialists in providing better interventions for the work integration of people with SMI. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".