Key items for reports of primary care research: an international Delphi study
Bibliographic record
Abstract
OBJECTIVE: Reporting guidelines can improve dissemination and application of findings and help avoid research waste. Recent studies reveal opportunities to improve primary care (PC) reporting. Despite increasing numbers of guidelines, none exists for PC research. This study aims to prioritise candidate reporting items to inform a reporting guideline for PC research. DESIGN: Delphi study conducted by the Consensus Reporting Items for Studies in Primary Care (CRISP) Working Group. SETTING: International online survey. PARTICIPANTS: Interdisciplinary PC researchers and research users. MAIN OUTCOME MEASURES: We drew potential reporting items from literature review and a series of international, interdisciplinary surveys. Using an anonymous, online survey, we asked participants to vote on and whether each candidate item should be included, required or recommended in a PC research reporting guideline. Items advanced to the next Delphi round if they received>50% votes to include. Analysis used descriptive statistics plus synthesis of free-text responses. RESULTS: 98/116 respondents completed round 1 (84% response rate) and 89/98 completed round 2 (91%). Respondents included a variety of healthcare professions, research roles, levels of experience and all five world regions. Round 1 presented 29 potential items, and 25 moved into round 2 after rewording and combining items and adding 2 new items. A majority of round 2 respondents voted to include 23 items (90%-100% for 11 items, 80%-89% for 3 items, 70%-79% for 3 items, 60%-69% for 3 items and 50%-59% for 3 items). CONCLUSION: Our Delphi study identified items to guide the reporting of PC research that has broad endorsement from the community of producers and users of PC research. We will now use these results to inform the final development of the CRISP guidance for reporting PC research.
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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 | 0.223 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".