Intersectoral action to employ individuals with mental illness: Lessons learned from a local development initiative
Bibliographic record
Abstract
BACKGROUND: Intersectoral action is now widely recognized as an effective approach to addressing the social determinants of health. In particular, collaboration between different sectors of the community has been recommended as a strategy for developing employment opportunities for persons diagnosed with mental illness. However, there is limited evidence on the actual implementation of intersectoral action between the employment and mental health sector. METHODS: Case study methodology was utilized to examine a unique partnership formed under the principles of public health and local development to create a social enterprise. Stakeholders representing organizations from several sectors of the community, including health and employment, partnered to develop work opportunities for a population that is disadvantaged from the mainstream employment market including (but not exclusive to) persons diagnosed with mental illness. The three main methods of inquiry were: semistructured interviews, participant observation and collected documentation. FINDINGS: Stakeholders experienced several kinds of challenges during the implementation process and used different strategies to manage these challenges. The findings suggest barriers and facilitators to successful intersectoral action initiatives, some of which are directly applicable to the context of employment and mental illness. CONCLUSION: Several lessons are drawn from these experiences.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".