Community Treatment Orders: The Service User Speaks. Exploring the Lived Experience of Community Treatment Orders
Bibliographic record
Abstract
This study uses an exploratory qualitative design to examine the lived experience of one group of service users on community treatment orders (CTOs). The study was designed and completed by four graduate students at Carleton University School of Social Work. Despite the unique features of CTO legislation in Ontario, many findings from this study are remarkably similar to findings of research conducted in other jurisdictions. What is unique in our findings is the lack of focus on the actual conditions and provision of the CTO. The issue for our participants was less about the CTO itself, and more about the labels, control and discrimination associated with severe mental illness. Cette étude utilise un concept qualitatif et exploratoire pour examiner les expériences vécues d’un groupe qui utilise les ordonnances de traitement en milieu communautaire (OTMC). Cette étude a été designée et complétée par 4 étudiants de l’école de service social de l’université Carleton. Malgré les nombreux aspects uniques de la loi gérant les OTMC de l’Ontario, plusieurs résultats de cette étude sont remarquablement similaires aux résultats découverts dans de différentes juridictions. L’élément unique de cette recherche est le manque de focus sur les conditions véritables et les provisions des OTMC. La problématique encourue par les participants n’était pas au sujet des OTMC en soi, mais plus tôt au sujet de l’étiquetage, du contrôle, et de la discrimination associé aux troubles de santé mentale sévères.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".