How is unemployment among people with mental illness conceptualized within social policy? A case study of the Ontario Disability Support Program
Bibliographic record
Abstract
BACKGROUND: Government policy shapes and is shaped by society's views of important social issues such as employment among people with disabilities. OBJECTIVE: This article explores how unemployment among people with mental illness has been understood and characterized within social policy. METHODS: Drawing on a qualitative case study that explored the construction and implementation of policy reform within the employment support branch of the Ontario Disability Support Program, this paper examines assumptions about unemployment among people with mental illness that underlie social policy and their impact on employment services and supports. RESULTS: The most prominent messages that emerged from the data focused on unemployment among people with mental illness as a function of personal responsibility, limitations and a lack of motivation. Although there was awareness of the role of social and systemic factors, these issues were given less weight, especially when describing employment support practices. There is a lack of sufficient attention to complex and deeply-rooted social and systemic inequalities within social policy and employment services. CONCLUSIONS: There is a need to expand conceptualizations of unemployment among people with mental illness within social policy, and develop interventions that address complex social factors and systemic constraints that can limit employment opportunities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".