Enabling careers, autonomy, and prosperity: Using community organizing and building approaches to improve the educational outcomes of people with mental illness
Bibliographic record
Abstract
OBJECTIVES: The objective of this regional initiative was to develop access to educational opportunities for people with mental illness with a view to ultimately advancing their career prospects. PARTICIPANTS: The initiative engaged a broad range of community stakeholders including people with mental illness, their families, educators, mental health service providers and, policy analysts. METHODS: The initiative used community organizing and development strategies to develop solutions to problems related to access to education. RESULTS: The initiative was successful in mobilizing community participation, identifying priorities, and translating these priorities into action plans. Working groups of community stakeholders engaged in initiatives related to improving access to resources to support education, developing training for teachers in secondary schools, creating peer support systems, and developing a pilot supported education program as a partnership between a college and mental health service. CONCLUSION: Organized community building provided a foundation for a broad range of initiatives meant to improve access to educational opportunities for people with mental illness. Evaluation efforts will need to focus on the extent to which these initiatives ultimately ledto positive changes in the careers of people 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| 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".