ICES Report: Performance Reporting and Public Health: Bridging the Gap
Bibliographic record
Abstract
cientists at the Institute for Clinical Evaluative Sciences (ICES) recently created the "smoking regulatory index" (SRI) to compare how well municipal by-laws protect people against the harmful health effects of second-hand smoke.We used publicly available information on current municipal by-laws in Canada, along with evidence that restrictions on smoking reduce adverse health effects from second-hand smoke, to measure the effectiveness of these public health policies.The results showed a wide range in the degree to which municipal by-laws protect their citizens -from almost no protection in many municipalities on the East Coast to complete protection on the West Coast.Typically, the municipalities that had the fewest by-laws had the highest smoking and heart disease rates -following the same East to West Coast gradient.Creating the SRI highlighted the issue of how little information actually exists that adequately describes public health services for smoking prevention or cessation in Canada.Despite the obvious significance of smoking as a health risk, the only national public information on smoking by-laws that we were able to find dated back to 2001, and many municipalities were not represented.We were able to find some details on tobacco pricing and taxes, but nothing that described other smoking prevention or cessation programs.Some of this information is available from local public health departments/ regions, but no information could be found that compared regions across Canada.In fact, there are widespread gaps across the board in available information about public health services and performance in Canada.There is almost no national information that describes which public health services are provided.And there is even less available to allow an evaluation of whether the levels of these public health services are sufficient enough or of high enough quality to be effective.
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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 | 0.184 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".