Communicating the benefits of population health interventions: The health effects can be on par with those of medication
Bibliographic record
Abstract
How can we communicate to the public that population level health interventions are effective at improving health? Perhaps the most familiar "currency" of effect is that which can be brought about via medication. Comparisons of effect sizes may be effective ways of communicating the benefits of population health interventions if they are seen and understood in the same way that medications are. We developed a series of comparisons to communicate benefits of population health interventions in terms of similar gains to be obtained from statins, metformin and antihypertensive medications for prevention of cardiovascular events, type 2 diabetes, obesity and hypertension. A purposive search identified evidence of population health intervention-related benefits. This evidence ranged from meta-analyses of RCTs to that from observational cohort studies. Population health interventions included implementation of national smoke free legislation, enhanced neighbourhood walkability, increased opportunities for active travel and protection of urban green space. In some cases, the benefits of population health interventions were found to be equivalent to, or even outweighed those of the medications to which they were compared. For example, RCT-based evidence suggested that exercise taken with a view of a green space was associated with 12 mmHg and 6 mmHg reductions in systolic and diastolic blood pressure, respectively, which was at least on par with the reductions associated with antihypertensive medications. Future work will test the effectiveness of these comparisons for increasing the familiarity, credibility and acceptability of population health interventions and, in particular, examine the importance of communicating putative mechanisms and potential co-benefits.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".