The Role of Evidence in Public Health Policy: An Example of Linkage and Exchange in the Prevention of Scald Burns
Bibliographic record
Abstract
Is sound evidence sufficient to change public health practice and policy?In this paper, we describe a campaign to reduce scald burns among children based on compelling evidence of the effectiveness of an intervention to reduce hot tap water temperature.We provide an overview of the problem and the evidence to support our efforts, the context for addressing the scald problem and the lessons learned about why the relationship between evidence and change in practice is not straightforward. RésuméDes preuves évidentes sont-elles suffisantes pour provoquer un changement dans les pratiques et politiques en matière de santé publique?Cet article décrit une campagne visant à réduire les brûlures par liquides chauds chez les enfants fondée sur les preuves indéniables de l' efficacité d'une intervention consistant à réduire la température de l' eau chaude du robinet.L' article offre une vue d' ensemble du problème des brûlures par liquides chauds ainsi que des données qui soutiennent les efforts accomplis, puis décrit le contexte de cette problématique pour finalement conclure sur les leçons apprises qui indiquent que le lien entre les preuves fournies et les changements de pratique n' est pas évident.T O N APRIL 3, 1875, A YOUNG GIRL NAMED MAGGIE WAS SCALDED BY HOT water from a pail.Maggie was the first patient of The Hospital for Sick Children in Toronto, Canada.To mark the 120th anniversary of the hospital, a health education campaign was launched with the theme of preventing scalds among children.Although much has changed over 120 years, hot water scalds remain a cause of preventable injuries to children.
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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 | 0.534 | 0.574 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.035 | 0.083 |
| Open science | 0.008 | 0.040 |
| Research integrity | 0.047 | 0.041 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".