Employment services for persons with serious mental illness in northeastern Ontario: The case for partnerships
Bibliographic record
Abstract
OBJECTIVE: To better understand why employment success is low, a case study was conducted to examine the influence of place on access to employment for persons with serious mental illness (SMI) residing in two northeastern Ontario communities (Rebeiro, in progress). METHODS: Community-based participatory research methods were used to engage persons who experience SMI, decision-makers and providers in the research. Forty-six interviews were conducted, complemented by primary and secondary quantitative data sources. RESULTS: While most consumers consider employment to be a key element of their recovery, employment rates for persons with SMI remain limited in northeastern Ontario, Canada. The findings of this case study reveal the importance of collaborative partnerships to fostering better employment outcomes in northeastern Ontario. CONCLUSION: The challenges of collaboration due to rural and northern tensions, as well as various jurisdictional and funding tensions existing at the level of community support the case for partnerships in the provision of employment services in northern and rural places.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".