Salient components in supported employment programs: Perspectives from employment specialists and clients
Bibliographic record
Abstract
Objective: This study aimed to identify the key components of supported employment (SE) programs needed to help people with serious mental illness obtain and maintain competitive employment. Participants and methods: Via convenience sampling, semi-structured interviews were conducted with 69 employment specialists and ninety-nine (99) clients who successfully obtained employment through SE programs in three Canadian provinces. Results: The findings describe five themes important to getting a job and to keeping a job: 1) philosophy of the program, 2) programmatic SE components, 3) employment specialists' competencies (skills, attitudes, and behaviours), 4) clients' skills and characteristics, and 5) elements related to employers. Employment specialists perceived a positive attitude and a client-centered program philosophy to be important for obtaining employment, while they perceived the support offered, the frequency and length of the follow-up as essential elements for maintaining a job. Clients perceived the employment specialists' competencies (e.g., positive attitude, marketing skills) to be important components. Conclusion: These results suggest a need to update the essential components in SE programs, or to include additional SE components.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".