Fit to the task: using Canadian data for evidence-informed public health
Bibliographic record
Abstract
The challenge of data gaps in public healthEvidence-informed decision-making (EIDM) is the practice of integrating scientific research evidence with the many other competing social and political considerations that inform policy (National Collaborating Centre for Methods and Tools, 2012).However, decision-makers and those who participate in policy development are often confounded in this effort by data that are patchy, weak, or missing altogether.We must often reach for the "next best thing, " typically data or studies from other demographically "comparable" nations, whom we assume to live, eat, work, and recreate in a manner closely similar to our Canadian population, under similar environmental conditions.This is problematic given that, within our own Canadian population, there is a wide disparity in factors influencing health, such as access to healthy foods, clean drinking water, and the presence of environmental contaminants.Also at issue is how we use data.Although it may be useful to know that a given intervention had a certain effect in some other population (i.e., a static study), having access to detailed health data collected repeatedly over time (i.e., surveillance data) allows us to not only make comparisons amongst populations and track changes in health status, but also predict the health impacts of specific interventions and evaluate them after implementation.The aim of this short commentary is to highlight two large, cross-sectional, representative Canadian data sets that have great potential to inform public health policy; one such example is the use of research around the built environment. Canadian data setsSignificant efforts have been made in recent years to build data resources that reflect our Canadian way of life and the environmental health challenges we face-data that are, in fact, fit to the task.Many of these data sets have been made available through the Government of Canada's Open Data Portal, which allows users to search for data sets by subject matter and key words.Among the many data sets already available, there are two largescale initiatives that have high value for understanding and addressing environmental health issues.
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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 | 0.310 | 0.656 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.042 | 0.052 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 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".