The Yorkhill CHIRPP story: a qualitative evaluation of 10 years of injury surveillance at a Scottish children’s hospital
Bibliographic record
Abstract
BACKGROUND: The Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) is an emergency department-based injury surveillance system that was devised in Canada and has been in operation since 1990. CHIRPP was imported to Glasgow's Royal Hospital for Sick Children at Yorkhill in 1996 and ran for 10 years. OBJECTIVE: To critically review CHIRPP at Yorkhill (Y-CHIRPP). The following two key questions were posed. (1) Did Y-CHIRPP fail, and, if so, why? (2) What generalisable lessons can be learned about injury surveillance? METHODS: A retrospective qualitative review of Y-CHIRPP was carried out. In gathering information, the aims were to: (a) describe the processes involved in running Y-CHIRPP; (b) identify changes made to that process over the 10 years; (c) determine the strengths and weaknesses of Y-CHIRPP. RESULTS: Taken together, and with reference to the WHO attributes of a good surveillance system, the findings suggest that Y-CHIRPP largely met the criteria of simplicity, flexibility, and acceptability. Criteria that were not, or only intermittently, met were reliability, utility, sustainability, and timeliness. CONCLUSIONS: Y-CHIRPP was, at best, a partial success. To maintain the viability of an injury surveillance system and to secure the long-term support of hospital staff, it is important that the system is perceived as an injury prevention service tool and not a research method. Experience with Y-CHIRPP suggests that injury surveillance requires three supporting posts: an emergency department staff member, a data analyst, and someone with responsibility for developing and/or lobbying for the implementation of preventive measures.
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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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".