Measuring Quality of Care Received by Suicide Attempters in the Emergency Department
Bibliographic record
Abstract
CONTEXT: Audits conducted on medical records have been traditionally used in hospitals to assess and improve quality of medical care but have yet to be properly integrated and used for suicide prevention purposes. We aimed to (1) revise a quality of care grid and adapt it to an adult population of suicide attempters and (2) identify quality of care deficits in managing adult suicide attempters at the emergency department (ED) in two different Montreal university hospitals. METHODS: An existing checklist for quality of medical and social care in the ED was adapted. A systematic search and data extraction of all suicide attempters in two different Montreal university hospitals were then conducted. All identified individuals who attempted suicide were fully reviewed and quality of care was assessed. RESULTS: Eleven criteria were kept by the expert focus group in the revised grid that was then used to rate 369 individuals that attempted suicide. Suicide risk assessment was only present in 63% of attempters before discharge. Although family history was documented for 90% of attempters, in only 41% of the cases were interviews conducted with relatives. Most discharged patient lacked proper follow-up considering 11% of their relatives received written information on resources in case of need. DISCUSSION: Paper records may be used to systematically assess the quality of care for suicide attempters seen in ED. Results reiterate the need for better suicide prevention strategies for these individuals. The checklist proved to be an excellent assessment of best practices or identification of possible improvements.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".