"Experience is the Best Teacher." Community Treatment Orders (CTOs) among Ethno-Racial Minority Communities in Toronto: A Phenomenological Study
Bibliographic record
Abstract
Since de-institutionalization, numerous community based treatment modalities have been implemented to provide treatment for individuals diagnosed as seriously and persistently mentally ill. CTOs are a recent addition to the community mental health care system designed to provide outpatient mental health services to seriously mentally ill clients and using legal mechanisms to enforce a contractual obligation to participate in those services. Although there is a growing body of literature on CTOs and other mandated outpatient treatment programs for people diagnosed with mental illnesses, the research predominantly focuses on the perspectives of service providers and family members. Little attention has been given to how clients view the experience of receiving the treatment and no attention has been given to the experience of clients who are of ethno-racial minority background. As Ontario is a racially and ethnically diverse environment in which many people of minority backgrounds are placed on CTOs. This study, utilizing a phenomenological methodology, interviewed twenty-four participants of ethno-racial minority background who are either on CTOs or have been on a CTO in the past. The focus of the study was to explore the views and lived experience of the participants regarding the treatment. The outcome of the study showed that the participants did not experience the treatment as racially motivated but felt it was necessary and beneficial. The participants discussed the impact of power in the treatment process. Implications of the study were that it would enhance the mental health literature by providing an understanding of serious mental illness among individuals of ethno-racial minority background. The study would provide insight for policy makers and practitioners on providing effective support for the marginalized.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".