“Stuck in the Mud”: Limited Employment Success of Persons With Serious Mental Illness in Northeastern Ontario
Bibliographic record
Abstract
Despite policy support, empirical evidence, investment in community mental health programs, and a good deal of rhetoric about promoting recovery, people with serious mental illness (SMI) remain disproportionately represented in paid employment, especially in northern and rural places in Ontario. This study examines access to employment through the perspectives of people with SMI, providers, and decision makers who reside in two northeastern Ontario case communities. A qualitative case study using community-based participatory research methods was employed. Data from interviews conducted with 46 participants were analyzed thematically and were complemented by a secondary data source reporting on the employment outcomes of 4,112 people with SMI. This paper reports on the qualitative findings, highlighting how employment for persons with SMI is stuck in the mud by a dominant community discourse conveying a disbelief in the capacity of people with SMI to be employed—a discourse sustained by a variety of local and systemic tensions in local practices. This study underscores the ineffectiveness of current employment programs for people who experience SMI in the case communities, and the need to develop local capacity to provide evidence-based practices to improve employment success, and subsequently, to shift marginalizing discourses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".