Understanding suicide risk assessment practices in psychotic disorders: insights from Canadian mental health professionals, a preliminary investigation
Bibliographic record
Abstract
BACKGROUND: In Canada, more than 4,500 people die by suicide annually, with individuals diagnosed with psychotic disorders being at significantly higher risk. Although the risk factors for suicide in this population are well-established, the assessment of suicide risk remains underexplored. AIM: This study examines the practices of mental health professionals working with clients with a psychotic disorder in relation to suicide risk assessment, using the Theory of Planned Behavior (TPB) as a theoretical framework. METHOD: A survey of 148 professionals across Canada was conducted to assess factors influencing the frequency and thoroughness of suicide risk assessments. RESULTS: Despite our perceived behavioral control scale having psychometrical flaws, results revealed that social norms were a significant predictor of systematic suicide risk assessments. Professionals identified a lack of time, training, and inadequate clinical tools as major obstacles to thorough assessments. Despite the widespread availability of suicide risk assessment training, many professionals did not feel adequately prepared or confident in conducting such assessments, particularly with clients with a psychotic disorder. CONCLUSION: The findings highlight the need for enhanced support, training, and organizational changes to improve the systematic assessment of suicide risk in this vulnerable population.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".