Interventions to Enable or Improve Evidence-Informed Decision Making in Public Health and Preventive Medicine: A Scoping Review
Bibliographic record
Abstract
Introduction: Public health and preventive medicine is the branch of medicine that implements population-level health interventions. Public health and preventive medicine specialists practice at the intersection of medicine and public health. These 2 disciplines approach the application of evidence to decision making differently, and it is unclear how public health and preventive medicine specialists are trained to navigate between these approaches. To better understand the interventions available to support public health and preventive medicine physicians in making evidence-informed decisions in this space, the authors conducted a scoping review to summarize planned or implemented interventions that support or are intended to support evidence-informed decision making in public health and preventive medicine practice. Methods: In March and April 2025, the authors searched multiple databases and prespecified websites to identify published and unpublished academic and gray literature reports published in English between 1992 and March 2025 that described interventions intended to enable or improve evidence-informed decision making in public health and preventive medicine practice. Data extracted included, where reported, publication date, authors, title, publication source, actual or intended participants, participant characteristics, intervention characteristics, funding sources, and intervention outcome. Results: The authors identified 246 records, 234 through database and website searches and 12 through citation searches, of which 9 were duplicates. Ultimately, the authors included 17 reports that described 7 unique interventions in this scoping review. The majority (11 of 17) of reports described interventions that took place in the U.S., and most (9 of 17) reports described an iteration of the Evidence-Based Public Health Course. All interventions reported success, most often in terms of positive participant reaction, self-reported participant learning, or participant behaviour change. One intervention, an organizational strategic plan that supported evidence-informed decision-making capacity, reported organizational- and population-level impacts. Discussion: This scoping review did not identify any interventions specifically targeting evidence-informed decision making in public health and preventive medicine, but there was some indication that there are differences between the types of evidence that physicians value most and those by other public health professionals. The components of evidence-informed decision making in public health implemented or taught in the interventions identified in this scoping review differ from the components of evidence-based medicine taught in undergraduate and postgraduate medical education, which has implications for physicians working in public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".