No-Suicide Agreements: Current Practices and Opinions in a Canadian Urban Health Region
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which no-suicide agreements (NSAs)--one method of intervening with people at risk of suicide--are used by a population of outpatient mental health therapists in a Canadian urban health region, and to describe therapists' perceptions and practices surrounding their use. METHOD: The survey was mailed to 516 therapists, including psychiatrists, psychologists, nurses, social workers, and occupational therapists. RESULTS: Completed surveys were returned by 312 therapists (response rate = 60.5%). NSAs were used by 83%, although 43% had no formal training in their use. Among those who had used NSAs, 31% reported having had at least one patient attempt or complete suicide while an agreement was in place. Therapists from nonmedical disciplines were most likely to have used these agreements. Most therapists believed NSAs communicated care and concern to patients. Respondents were divided in their perceptions of whether NSAs afforded liability protection in the event of a patient suicide. Contextual factors associated with the perceived degree of suicide risk, the patient-therapeutic relationship influenced a therapist's use of NSAs. Most therapists attempted to have patients admitted to hospital if the patient refused to enter into an NSA. CONCLUSIONS: Use of NSAs is prevalent in this population of outpatient psychotherapists, suggesting that these therapists believe they are a useful intervention in the management of suicidal patients. Practitioners might benefit from increased formal training opportunities in the use and legal implications of NSAs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".