Right to Appeal, Non-Treatment, and Violence Among Forensic and Civil Inpatients Awaiting Incapacity Appeal Decisions in Ontario
Bibliographic record
Abstract
Background: Mental health legislation in Ontario, Canada, permits inpatients to refuse treatment while appealing their incapacity finding to the Consent and Capacity Board (CCB). Lack of treatment during this period poses safety concerns, as inpatients who remain untreated are at higher risk of engaging in violent behavior. The present study explored the relationship between non-treatment and violence among forensic and civil inpatients awaiting their CCB hearing at the largest psychiatric hospital in Canada. Methods: We investigated the electronic health records of 285 inpatients whose CCB applications were heard between 2014 and 2016 to better understand violent outcomes among inpatients and determine whether application timelines differed between forensic and civil inpatients. Results: Four key findings were observed. First, forensic inpatients had more episodes of violence requiring seclusion and restraint during the application timeline compared with civil inpatients. Second, forensic inpatients waited longer than civil inpatients for their appeal to be heard at the CCB. Third, the most ill inpatients, indexed by degree of comorbidity, waited the longest for their CCB appeal. Finally, unwillingness to accept PRN medications was a potent risk factor for violence among all inpatients. Conclusions: Compared with civil inpatients, forensic inpatients waited longer for CCB appeals. They also scored higher on one measure of violent behavior. Further research should explore the clinical implications of non-treatment for higher-risk groups, such as forensic inpatients and inpatients with comorbidities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".