Towards systematic reviews that inform health care management and policy-making
Bibliographic record
Abstract
OBJECTIVES: To identify ways to improve the usefulness of systematic reviews for health care managers and policy-makers that could then be evaluated prospectively. METHODS: We systematically reviewed studies of decision-making by health care managers and policy-makers, conducted interviews with a purposive sample of them in Canada and the United Kingdom (n = 29), and reviewed the websites of research funders, producers/purveyors of research, and journals that include them among their target audiences (n = 45). RESULTS: Our systematic review identified that factors such as interactions between researchers and health care policy-makers and timing/timeliness appear to increase the prospects for research use among policy-makers. Our interviews with health care managers and policy-makers suggest that they would benefit from having information that is relevant for decisions highlighted for them (e.g. contextual factors that affect a review's local applicability and information about the benefits, harms/risks and costs of interventions) and having reviews presented in a way that allows for rapid scanning for relevance and then graded entry (such as one page of take-home messages, a three-page executive summary and a 25-page report). Managers and policy-makers have mixed views about the helpfulness of recommendations. Our analysis of websites found that contextual factors were rarely highlighted, recommendations were often provided and graded entry formats were rarely used. CONCLUSIONS: Researchers could help to ensure that the future flow of systematic reviews will better inform health care management and policy-making by involving health care managers and policy-makers in their production and better highlighting information that is relevant for decisions. Research funders could help to ensure that the global stock of systematic reviews will better inform health care management and policy-making by supporting and evaluating local adaptation processes such as developing and making available online more user-friendly 'front ends' for potentially relevant systematic reviews.
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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 | 0.857 | 0.899 |
| Meta-epidemiology (narrow) | 0.008 | 0.015 |
| Meta-epidemiology (broad) | 0.021 | 0.015 |
| Bibliometrics | 0.062 | 0.038 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.060 | 0.073 |
| Open science | 0.019 | 0.038 |
| Research integrity | 0.037 | 0.040 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".