Operationalizing the ‘population health’ approach to permit consideration and minimization of unintended harms of public health interventions: a malaria control example
Bibliographic record
Abstract
To achieve elimination of malaria, both ‘populations at risk’ strategies and ‘population health’ approaches to intervention are required. While the ‘populations at risk’ vs. ‘population health’ debate is not new to public health, here we advance the discussion by identifying how the ‘population health approach’, coupled with concepts from theories of unintended harms, could be used to identify and guide efforts to minimize unintended harm associated with ‘populations at risk’ strategies, using malaria as an example. We begin by reviewing unintended harm and present the presumptive diagnosis and treatment of malaria clinical practice guideline (PDTM-CPG) as an example of a ‘populations at risk’ strategy for malaria control. We then consider the value of the ‘population health’ approach for identifying and minimizing cultural and economic unintended harms associated with the PDTM-CPG. We outline several concepts that are helpful in terms of the identification and mitigation of unintended harm. Specifically, the ‘population health approach’ emphasizes structural determinants of health that are key to enhancing intervention impact and reducing inequities, while theories of unintended harms emphasize factors that play into the selection and impact of interventions; namely, the breadth and depth of the knowledge base, contextual considerations, basic values, and the perceived need for immediate action. Finally, based on these key concepts, we identify practical discussion questions for district, national, and international public health planners and policy-makers to reflect upon when engaging in intervention design or adaptation. These questions are intended to maximize efforts to achieve malaria elimination while minimizing unintended harms.
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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 | 0.040 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".