Critical Analysis of a Population Mental Health Strategy: Effects on Stigma for People With Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Introduction: Stigma is widely identified as an issue affecting the health and well-being of people with intellectual and developmental disabilities (IDD), and those with mental illnesses. To address this issue, a population mental health strategy, which includes a focus on reducing stigma and discrimination, was developed by the government of Ontario, Canada. Methods: Drawing on a critical policy analysis approach, we examined how stigma and mental illness are problematized and addressed in this strategy, and the implications for people with co-occurring IDD and mental illness. Results: Our analysis revealed that implicit understandings of exclusion, dependency, and inadequate economic participation have shaped how the explicitly identified issues of stigma and discrimination related to mental illness are problematized in the strategy. These implicit understandings have shaped courses of action (e.g., mental health promotion and anti-stigma programs) that may have positive consequences (e.g., skills for managing one’s own mental health, decreased discrimination related to mental illness), but may have unintended negative consequences (e.g., stigma of being different and doing different than people without IDD and/or mental illness, and not being able to support oneself). Conclusions: Acknowledging disability discrimination and emphasizing other ways of participating and being included in communities that are not predominantly focused on independence and productivity can potentially contribute to reducing the stigma experienced by people with concurrent IDD and mental illness, and improve their health and well-being.
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 | 0.045 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".