Smoke and mirrors : reflections of policy and practice for those with a mental illness and who are in conflict with the law
Bibliographic record
Abstract
This study examined the use of language in the development and implementation of mental health policy. It focused on the current discourse of mental health reform in Ontario as it related to individuals with a mental illness and who are in conflict with the law. Using a qualitative design, informed by critical inquiry and a postmodern perspective, the researcher explored administrative perceptions of the accomplishments and challenges faced at different levels of the mental health and criminal justices systems in Ontario. The participants' understandings of the provincial mental health reform policy, Making it Happen, and the extent they felt that their organizations and related policies were able to create positive change in the lives of service users were also examined. While the language of mental health policy encompasses an empowerment, community integration approach to providing services, findings indicated that a biomedical-model, public safety discourse appear to inform both policy and practice. A number of questions and apparent inconsistencies in the manner in which the mental health and criminal justice systems deal with the needs of this population were also identified. This thesis concludes with recommendations for future research.
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.052 | 0.052 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".