Self-Inflicted Injury-Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP-SI): a new surveillance tool for detecting self-inflicted injury events in emergency departments
Bibliographic record
Abstract
OBJECTIVES: To assess the performance of the Canadian Hospitals Injury Reporting and Prevention Program's newly developed self-harm surveillance tool (CHIRPP-SI) designed to improve emergency department (ED) hospital surveillance of youth self-inflicted injury (SI). METHODS: This was a prospective, single-centre cohort study from February 2015 to September 2015. Eligible participants were aged 6-17.99 years and presented to the ED with a primary mental health complaint. The frequency of SI cases was extracted from three data sources (CHIRPP-SI, medical chart, and the National Ambulatory Care Reporting System Metadata (NACRS)). Cohen's kappa statistic was used to examine the level of agreement between data sources. RESULTS: Of the 250 participants who received a medical chart review, 70 completed the CHIRPP-SI. Of those who did not complete the CHIRPP-SI, 86% (n = 154) reported no SI related to their presentation, 12% (n = 22) declined to participate without specifying self-injury status, and 2% (n = 4) were unable to be interviewed prior to discharge. The three sources of surveillance data varied considerably; the medical chart captured the highest frequency of individuals reporting SI related to their ED visit (33.6%), followed by the CHIRPP-SI (28.0%), and the NACRS database (8.4%). The CHIRPP-SI captured the method of SI and the place of occurrence in 100% of individuals, and the bodily location harmed in 98.6% of individuals. CONCLUSIONS: Study findings highlight the disparity between different sources of data, in relation to the capture of paediatric SI, presenting to hospital EDs. If greater details of SI events are to be identified, surveillance tools such as the CHIRPP-SI should be considered.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".