Proceedings from the 2024 precision public health research symposium: advancing health equity through precision public health
Bibliographic record
Abstract
While precision public health (PPH) has emerged as a population-level approach that allows for tailoring of prevention and health promotion strategies by delivering the right intervention to the right population at the right time, concerns have been raised about whether this type of approach may exacerbate health inequalities. Key challenges include an overemphasis on individual agency to accomplish precision-based prevention activities and potential to divert limited resources away from established drivers of public health. Another challenge is limitations to risk prediction models due to limited data or knowledge, leading to differences in prediction accuracy between subgroups (and thus inequitable benefits from risk prediction), or even potential harms if predictions for some subgroups are inaccurate and misleading.
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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 | 0.074 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.036 | 0.033 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".