Accounting for context: Social enterprises and meaningful employment for people with mental illness
Bibliographic record
Abstract
BACKGROUND: Many people living with mental illness want paid work, but finding and maintaining mainstream employment remains challenging. In recent decades, social enterprises have emerged as one alternative site for paid employment. Existing research has examined the experiences of people with mental illness working in social enterprises, but less is known about the organizational character of these workplaces. OBJECTIVE: The objective of this paper is to develop a better understanding of social enterprises as organizational contexts for workers with mental illness. METHODS: The research employed a qualitative methodology, conducting semi-structured interviews with executive directors and managers at 42 organizations operating 67 social enterprises across CanadaRESULTS:While there are strong similarities in organizational mandate to create meaningful employment there are also important variations between social enterprises. These include variations in size, economic activity and organizational structure, as well as differences in hours of work, rates of pay and the nature and extent of workplace accommodation. These variations reflect both immediate organizational contexts as well as broader economic constraints that enterprises confront. CONCLUSIONS: Understanding the varied nature of social enterprises is important for thinking about future enterprise development, and the capacity of such organizations to create meaningful employment for people living with mental illness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".