Using Natural Experiments to Evaluate the Potential Public Health Benefits of the Toronto Cold Weather Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extreme cold weather alert programs have been implemented in some areas to address the significant health impacts of exposure to cold. One such program is the Toronto Cold Weather Program (TCWP) that was implemented in the City of Toronto since 1996 to protect the public from extreme weather conditions. In this paper, we aim to evaluate the effectiveness of the TCWP in reducing mortality and morbidity outcomes related to cold temperatures. We applied a quasi-experimental study design using the Difference-in-Differences method coupled with propensity-score-matching to determine the effect of the TCMP on daily hospitalizations and deaths due to cardiovascular disease (CVD), coronary heart disease (CHD) or cerebrovascular disease, using two complementary analytical approaches. Overall, the analysis did not detect an impact on reduced mortality/morbidity in the City of Toronto from the TCMP. For example, we obtained a Risk Difference (RD) of -0.88 (per 1,000,000 people) (95% CI: -3.27 to 1.51) and a Risk Ratio (RR) of 0.98 (95% CI: 0.91 to 1.05) people for CVD hospitalizations. The TCWP was not found to be effective in reducing cold related mortality and morbidity which demonstrates the importance of improving existing policies related to cold in Canada and other countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it