Evidence-informed health policy 1 – Synthesis of findings from a multi-method study of organizations that support the use of research evidence
Bibliographic record
Abstract
BACKGROUND: Organizations have been established in many countries and internationally to support the use of research evidence by producing clinical practice guidelines, undertaking health technology assessments, and/or directly supporting the use of research evidence in developing health policy on an international, national, and state or provincial level. Learning from these organizations can reduce the need to 'reinvent the wheel' and inform decisions about how best to organize support for such organizations, particularly in low- and middle-income countries (LMICs). METHODS: We undertook a multi-method study in three phases - a survey, interviews, and case descriptions that drew on site visits - and in each of the second and third phases we focused on a purposive sample of those involved in the previous phase. We used the seven main recommendations that emerged from the advice offered in the interviews to organize much of the synthesis of findings across phases and methods. We used a constant comparative method to identify themes from across phases and methods. RESULTS: Seven recommendations emerged for those involved in establishing or leading organizations that support the use of research evidence in developing health policy: 1) collaborate with other organizations; 2) establish strong links with policymakers and involve stakeholders in the work; 3) be independent and manage conflicts of interest among those involved in the work; 4) build capacity among those working in the organization; 5) use good methods and be transparent in the work; 6) start small, have a clear audience and scope, and address important questions; and 7) be attentive to implementation considerations, even if implementation is not a remit. Four recommendations emerged for the World Health Organization (WHO) and other international organizations and networks: 1) support collaborations among organizations; 2) support local adaptation efforts; 3) mobilize support; and 4) create global public goods. CONCLUSION: This synthesis of findings from a multi-method study, along with the more detailed findings from each of the three phases of the study (which are reported in the three following articles in the series), provide a strong basis on which researchers, policymakers, international organizations (and networks) like WHO can respond to the growing chorus of voices calling for efforts to support the use of research evidence in developing health policy.
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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 | 0.325 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".