Challenges of Suicide Risk Assessment in Emergency Rooms
Bibliographic record
Abstract
Suicidal behaviour is one of the most common reasons for presentation to the emergency rooms. Perhaps the most frequently examined topic in the field of suicidology, is the degree to which death by suicide can be predicted. Moreover, some suicide risk factors may not be included consistently in the suicidal risk assessments in the emergency room. Understand the suicide risk predictors that are most important in decision making in the emergency room, risk factors that often get missed in the emergency room assessments We aim to use the results of this study to implement educational intervention that gears towards improving suicidal risk assessment and documentation in emergency room. An online survey was sent to all psychiatry and emergency physicians at Queen's university to assess their opinion on predictors of suicide while assessing patients. The importance of predictors was compared between 2 groups. In addition charts of all patients assessed for suicide risk were reviewed. Suicide predictors assessed, the clinical decision made and the suicide predictors missed at the time of assessment, were recorded. Our study shows that although there is a significant links between bullying and childhood trauma, and suicidal behaviour, these predictors were not commonly assessed. The result of our study also shows that many predictors deemed important by physicians are missed on actual assessment. Our study shows that many important suicidal risk factors are missed in emergency room assessments. It is important that physicians identify these risk factors while assessing suicidality.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".