Using research to inform healthcare managers' and policy makers' questions: from summative to interpretive synthesis.
Bibliographic record
Abstract
This paper highlights the importance of research synthesis for healthcare managers' and policy makers' questions and the difficulty of generalizing from the methods used to answer clinicians' questions. Social science research has a central role in such syntheses because of the context-dependent nature of managers' and policy makers' questions, which generally encompass a far broader spectrum than the circumscribed "what works?" questions of clinically oriented reviews. A major challenge is in moving from purely researcher-driven processes, which summarize research, to co-production processes, which allow managers and policy makers to join with researchers in interpreting implications for the healthcare system. Additional challenges lie in clearly defining the function, role and objective of the synthesis; handling flexibility around finalizing the question; harnessing a manageable scope of literature to review; adopting rules to select the final sample of research; creating useful messages; and developing a format that is responsive to the needs and preferences of the audience. One inevitable conclusion is that research synthesis for managers and policy makers will, compared to that for clinicians, leave much discretion in the hands of the synthesiser(s). This raises the interesting issue of how to engender, in the absence of "methodological checklists," trust and credibility in both the people doing the synthesis and the processes they use.
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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 | 0.624 | 0.754 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.007 | 0.008 |
| 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".