Coercion in Outpatients under Community Treatment Orders: A Matched Comparison Study
Bibliographic record
Abstract
OBJECTIVE: Since the deinstitutionalization of psychiatric services around the world, the scope of outpatient psychiatric care has also increased to better support treatment access and adherence. For those with serious mental illness who may lack insight into their own illness, available interventions include coercive community practices such as mandated community treatment orders (CTOs). This paper examines the perceptions of coercion among service users treated with a CTO. METHOD: = 69 in each group). Participants were interviewed using a series of questionnaires aimed at evaluating their perceptions of coercion and other aspects of the psychiatric treatment. RESULTS: The level of coercion reported by service users treated under a CTO was significantly higher than that in the comparison group. However, in adjusted analyses, service users' perception of coercion, irrespective of their CTO status, was directly correlated with their previous experience with probation and inversely correlated with the sense of procedural justice in their treatment. CONCLUSIONS: Evaluation of psychiatric service users' experiences of coercion should consider their past and current involvement with other types of coercive measures, particularly history of probation. Clinicians may be able to minimize these experiences of coercion by incorporating procedural justice principles into their practice.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".