Assessment of underreporting of suicide attempts treated in emergency departments in Montreal and suggestions for improvement
Bibliographic record
Abstract
The goal of this study is to determine the potential of using hospital emergency department computer systems as tools to monitor suicide attempts treated in these emergency rooms. More specifically, the objectives are to draw up an inventory of the various computer systems used in hospitals and to evaluate the validity of the data collected, comparing them with information included in the patients' files. After completing a tour of emergency rooms in general hospitals, we put together a profile of the different computer systems used and verified the nature of the information contained in these systems concerning suicide attempts. We conducted a pilot project to estimate the scope of underreporting of suicide attempts by comparing data in the computer systems with that in patient files. Analysis of the utilisation of computer systems in hospitals revealed that the information recorded varies widely. Moreover, a comparison of the information available in these systems with that included in medical files shows that data validity and reliability are problematic. We suggest several strategies to improve the validity of data on suicide attempts, including training and increasing awareness of staff in emergency departments, and other incentive measures so that computer systems can be standardised. Language: en
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".