Bibliographic record
Abstract
CONTEXT: Selection of priority groups is important for health interventions. However, no quantitative method has been developed. OBJECTIVES: To develop a quantitative method to support the process of selecting priority groups for public health interventions based on both high risk and population health burden. DESIGN: Secondary data analysis of the 2010 Canadian Community Health Survey. SETTING: Canadian population. PARTICIPANTS: Survey respondents. METHODS: We identified priority groups for 3 diseases: heart disease, stroke, and chronic lower respiratory diseases. Three measures--prevalence, population counts, and adjusted odds ratios (OR)--were calculated for subpopulations (sociodemographic characteristics and other risk factors). A Priority Group Index (PGI) was calculated by summing the rank scores of these 3 measures. RESULTS: Of the 30 priority groups identified by the PGI (10 for each of the 3 disease outcomes), 7 were identified on the basis of high prevalence only, 5 based on population count only, 3 based on high OR only, and the remainder based on combinations of these. The identified priority groups were all in line with the literature as risk factors for the 3 diseases, such as elderly people for heart disease and stroke and those with low income for chronic lower respiratory diseases. The PGI was thus able to balance both high risk and population burden approaches in selecting priority groups, and thus it would address health inequities as well as disease burden in the overall population. CONCLUSIONS: The PGI is a quantitative method to select priority groups for public health interventions; it has the potential to enhance the effective use of limited public resources.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".