Evidence attack in public health: Diverse actors’ experiences with translating controversial or misrepresented evidence in health policy and systems research
Bibliographic record
Abstract
Bringing evidence into policy and practice discussions is political; more so when evidence from health studies or programme data are deemed controversial or unexpected, or when results are manipulated and misrepresented. Furthermore, opinion and misinformation in recent years has challenged our notions about how to achieve evidence-informed decision-making (EIDM). Health policy and systems (HPS) researchers and practitioners are battling misrepresentation that only serves to detract from important health issues or, worse, benefit powerful interests. This paper describes cases of politically and socially controversial evidence presented by researchers, practitioners and journalists during the Health Systems Research Symposium 2020. These cases cut across global contexts and range from public debates on vaccination, comprehensive sexual education, and tobacco to more inward debates around performance-based financing and EIDM in refugee policy. The consequences of engaging in controversial research include threats to commercial profit, perceived assaults on moral beliefs, censorship, fear of reprisal, and infodemics. Consequences for public health include research(er) hesitancy, contribution to corruption and leakage, researcher reflexivity, and ethical concerns within the HPS research and EIDM fields. Recommendations for supporting researchers, practitioners and advocates include better training and support structures for responding to controversy, safe spaces for sharing experiences, and modifying incentive structures.
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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 | 0.395 | 0.388 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.046 | 0.124 |
| Scholarly communication | 0.071 | 0.045 |
| Open science | 0.007 | 0.066 |
| Research integrity | 0.028 | 0.041 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".