Individual and environmental factors related to job satisfaction in people with severe mental illness employed in social enterprises
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to enhance understanding of the impact of individual and environmental variables on job satisfaction among people with severe mental illness employed in social enterprises. PARTICIPANTS: A total of 248 individuals with severe mental illness employed by social enterprises agreed to take part in the study. METHODS: We used logistic regression to analyse job satisfaction. A model with job satisfaction as the dependent variable, and both individual (occupational self-efficacy and severity of symptoms perceived) and environmental (workplace) factors (provision of workplace accommodations, social support from co-workers, organizational constraints) as well as external factors (family support) as predictors, was tested on the entire sample. RESULTS: All findings across the study suggest a significant positive impact of both individual and environmental factors on job satisfaction. People with higher occupational self-efficacy who were provided with workplace accommodations and received greater social support were more likely to experience greater job satisfaction. CONCLUSIONS: These results suggest that certain features of social enterprises, such as workplace accommodations, are important in promoting job satisfaction in people with severe mental illness. Further studies are warranted to expand knowledge of the workplace features that support employees with severe mental illness in their work integration process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".