Rose's population strategy of prevention need not increase social inequalities in health
Bibliographic record
Abstract
Geoffrey Rose's 1985 paper, Sick individuals and sick populations, continues to spark debate and discussion. Since this original publication, there have been two notable challenges to Rose's population strategy of prevention. First, identification of high-risk individuals has improved considerably in accuracy, which some believe obviates the need for population-wide prevention strategies. Secondly, and more recently, it has been suggested that population strategies of prevention may inadvertently worsen social inequalities in health. We argue that population prevention will not necessarily worsen social inequalities in health, and the likelihood of it doing so will depend on whether the prevention strategy is more structural (targets conditions in which behaviours occur) or agentic (targets behaviour change among individuals) in nature. Also, there are potential drawbacks of approaches that focus on discrete populations (i.e. high risk or vulnerable) that need to be considered when selecting a strategy. Although Rose's ideas need to be continually scrutinized, his population strategy of prevention still holds considerable merit for improving population health and narrowing social inequalities in health.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".