Evidence-informed health policy 2 – Survey of organizations that support the use of research evidence
Bibliographic record
Abstract
BACKGROUND: Previous surveys of organizations that support the development of evidence-informed health policies have focused on organizations that produce clinical practice guidelines (CPGs) or undertake health technology assessments (HTAs). Only rarely have surveys focused at least in part on units that directly support the use of research evidence in developing health policy on an international, national, and state or provincial level (i.e., government support units, or GSUs) that are in some way successful or innovative or that support the use of research evidence in low- and middle-income countries (LMICs). METHODS: We drew on many people and organizations around the world, including our project reference group, to generate a list of organizations to survey. We modified a questionnaire that had been developed originally by the Appraisal of Guidelines, Research and Evaluation in Europe (AGREE) collaboration and adapted one version of the questionnaire for organizations producing CPGs and HTAs, and another for GSUs. We sent the questionnaire by email to 176 organizations and followed up periodically with non-responders by email and telephone. RESULTS: We received completed questionnaires from 152 (86%) organizations. More than one-half of the organizations (and particularly HTA agencies) reported that examples from other countries were helpful in establishing their organization. A higher proportion of GSUs than CPG- or HTA-producing organizations involved target users in the selection of topics or the services undertaken. Most organizations have few (five or fewer) full-time equivalent (FTE) staff. More than four-fifths of organizations reported providing panels with or using systematic reviews. GSUs tended to use a wide variety of explicit valuation processes for the research evidence, but none with the frequency that organizations producing CPGs, HTAs, or both prioritized evidence by its quality. Between one-half and two-thirds of organizations do not collect data systematically about uptake, and roughly the same proportions do not systematically evaluate their usefulness or impact in other ways. CONCLUSION: The findings from our survey, the most broadly based of its kind, both extend or clarify the applicability of the messages arising from previous surveys and related documentary analyses, such as how the 'principles of evidence-based medicine dominate current guideline programs' and the importance of collaborating with other organizations. The survey also provides a description of the history, structure, processes, outputs, and perceived strengths and weaknesses of existing organizations from which those establishing or leading similar organizations can draw.
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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 | 0.054 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".