Improving core outcome set development: qualitative interviews with developers provided pointers to inform guidance
Bibliographic record
Abstract
OBJECTIVES: The objective of the study was to explore core outcome set (COS) developers' experiences of their work to inform methodological guidance on COS development and identify areas for future methodological research. STUDY DESIGN AND SETTING: Semistructured, audio-recorded interviews with a purposive sample of 32 COS developers. Analysis of transcribed interviews was informed by the constant comparative method and framework analysis. RESULTS: Developers found COS development to be challenging, particularly in relation to patient participation and accessing funding. Their accounts raised fundamental questions about the status of COS development and whether it is consultation or research. Developers emphasized how the absence of guidance had affected their work and identified areas where guidance or evidence about COS development would be useful including, patient participation, ethics, international development, and implementation. They particularly wanted guidance on systematic reviews, Delphi, and consensus meetings. CONCLUSION: The findings raise important questions about the funding, status, and process of COS development and indicate ways that it could be strengthened. Guidance could help developers to strengthen their work, but over specification could threaten quality in COS development. Guidance should therefore highlight common issues to consider and encourage tailoring of COS development to the context and circumstances of particular COS.
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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 | 0.202 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".