Predictors of Acquisition of Competitive Employment for People Enrolled in Supported Employment Programs
Bibliographic record
Abstract
This study aims at assessing the relative contribution of employment specialist competencies working in supported employment (SE) programs and client variables in determining the likelihood of obtaining competitive employment. A total of 489 persons with a severe mental illness and 97 employment specialists working in 24 SE programs across three Canadian provinces were included in the study. Overall, 43% of the sample obtained competitive work. Both client variables and employment specialist competencies, while controlling for the quality of SE programs implementation, predicted job acquisition. Multilevel analyses further indicated that younger client age, shorter duration of unemployment, and client use of job search strategies, as well as the working alliance perceived by the employment specialist, were the strongest predictors of competitive employment for people with severe mental illness, with 51% of variance explained. For people with severe mental illness seeking employment, active job search behaviors, relational abilities, and employment specialist competencies are central contributors to acquisition of competitive employment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".