Public but ineffective: Fatality inquiries into childhood deaths in Alberta
Bibliographic record
Abstract
ABSTRACT Introduction Child Death Review (CDR) processes are public efforts to review a child’s death to understand how and why children die, to improve child health and to prevent future deaths. In 2013, the Canadian Paediatric Society made specific recommendations to establish structured and comprehensive CDR systems in each province and territory. In Alberta, there is no comprehensive CDR process but there are some components. The most public and probably the most expensive component is the public fatality inquiry. A new notification policy adopted after in June 2017 appears to be of limited value for child death prevention. Methods We examined all Alberta fatality inquiry reports from January 1, 1995 to April 15, 2023 concerning children aged 0-17 years (n=133) to determine whether the fatality inquiry system might be effective in preventing future similar deaths. Results Recommendations made by judges in a fatality inquiry were not always followed by action, and hence inquiry recommendations have been largely ineffective. Fatality inquiry recommendations were sometimes untimely, and therefore had little chance of being effective. There is an increasing trend from 1995 (case 1) to 2023 (case 133) in the time taken to initiate a child fatality inquiry review. Discussion and Conclusion Information and recommendations from fatality inquiries into Alberta childhood deaths tend to be delayed and not followed by action. A comprehensive CDR process is required in Alberta. With system changes, public fatality inquiries could be an effective part of the child death prevention process.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".