Effectiveness of Public Health Interventions in Reducing Morbidity and Mortality during Heat Episodes: a Structured Review
Bibliographic record
Abstract
Increasing concern over the impact of hot weather on health has fostered the development of public health interventions to reduce heat-related health impacts. However, evidence of the effectiveness of such interventions is rarely cited for justification. Our objective was to review peer-reviewed and grey literature evaluating interventions aimed at reducing morbidity and/or mortality in populations during hot weather episodes. Among studies considering public risk perceptions, most respondents were aware when an extreme heat episode was occurring but did not necessarily change their practices, primarily due to a lack of self-perception as vulnerable and confusion about the appropriate actions to be taken. Among studies of health outcomes during and following heat episodes, studies were suggestive of positive impacts in reducing morbidity and mortality. While the limited evaluative work to date suggests a positive impact of public health interventions, concern persists about whether the most vulnerable groups, like the elderly and homeless, are being adequately reached.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".