The shift to rapid job placement for people living with mental illness: An analysis of consequences.
Bibliographic record
Abstract
OBJECTIVE: This article reports on the consequences of the revised policy for employment supports within the Ontario Disability Support Program, a disability benefit program administered by the provincial government in Ontario, Canada. The revised policy involves a change from a fee-for-service model to an outcome-based funding model. This revision has encouraged a shift from preemployment to job placement services, with a particular focus on rapid placement into available jobs. METHOD: Using a qualitative case study approach, 25 key informant interviews were conducted with individuals involved in developing or implementing the policy, or delivering employment services for individuals living with mental illness under the policy. Policy documents were also reviewed in order to explore the intent of the policy. Analysis focused on exploring how the policy has been implemented in practice, and its impact on employment services for individuals living with mental illness. RESULTS: The findings highlight how employment support practices have evolved under the new policy. Although there is now an increased focus on employment rather than preemployment supports, the financial imperative to place individuals into jobs as quickly as possible has decreased attention to career development. Jobs are reported to be concentrated at the entry-level with low pay and little security or benefits. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: These findings raise questions about the quality of employment being achieved under the new policy, highlight problems with adopting selected components of evidence-based approaches, and begin to explicate the influence that funding structures can have on practice.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".