Reporting guidelines for primary care research - what are the needs?
Bibliographic record
Abstract
Context: There is increasing interest in increasing the reliability and transparency of published health research. Despite a plethora of reporting guidelines published in recent years, no specific guidance exists for the reporting of primary care research. Objective: To assess how often the reporting of primary care research is problematic for researchers and other end-users. Design: Online survey (Qualtrics), five-point Likert scales (Always – Never), open questions. Setting: International, interdisciplinary primary care research community in late 2018. Participants: 286 respondents (113 USA, 47 Australia, 14 UK, 12 the Netherlands). 153 family physicians, 158 with a doctoral degree, 204 researcher/investigators, 20 patients. Findings: 51 found research findings difficult to implement about half the time due to the reporting. Qualitative studies were most problematic (63 said reports were insufficient at least half the time). 56 said reports were insufficient for meta-analysis most of the time and applying research to clinical practice, policy and teaching was also a problem at times. Reports did not always outline the theory informing the research or patient involvement. Contextual information about patients, practitioners, and health systems were emphasised as important issues. Implication(s) for practice: These initial results demonstrate unmet needs that may be met by the development of primary care research reporting guidelines. Our international group is working to develop Consensus Reporting Items for Studies in Primary Care.
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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 | 0.667 | 0.878 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.037 | 0.035 |
| Open science | 0.016 | 0.015 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".