Work accommodations and natural supports for maintaining employment.
Bibliographic record
Abstract
OBJECTIVE: Job tenure for people with severe mental disorders, even for those enrolled in supported employment programs, is typically brief. Few studies to date have investigated the relationship between accommodations and natural supports available in the workplace, and job tenure for this population. The main objectives of this study were to develop and to validate a new measure to describe work accommodations and natural supports available in the workplace and to determine which of them are significantly related to job tenure for participants enrolled in supported employment services. METHODS: In total, 124 people with a severe mental disorder enrolled in supported employment programs and who obtained only one competitive employment at the 9-month follow-up answered the Work Accommodation and Natural Support Scale (WANSS). They also provided information regarding their disclosure (or non-) of mental disorders in the workplace and the length of their job tenure. RESULTS: Confirmatory factor analysis conducted on the WANSS showed 40 items distributed on 6 dimensions (e.g., Schedule flexibility). Correlation results showed that disclosure was significantly related to the number of work accommodations and natural supports available in the workplace. Survival analyses indicated that one WANSS dimension was more salient in predicting job tenure: Supervisor and coworker supports. CONCLUSION AND IMPLICATION FOR PRACTICE: The WANSS is a valid and useful tool to assess work accommodations and natural supports available in the workplace that employment specialists could use in their practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".