Improving the Reporting of Primary Care Research: An International Survey of Researchers
Bibliographic record
Abstract
PURPOSE: To assess opportunities to improve reporting of primary care (PC) research to better meet the needs of its varied users. METHODS: International, interprofessional online survey of PC researchers and users, 2018 to 2019. Respondents used Likert scales to rate frequency of difficulties in interpreting, synthesizing, and applying PC research reports. Free-text short answers were categorized by template analysis to record experiences, concerns, and suggestions. Areas of need were checked across existing reporting guidelines. RESULTS: Survey yielded 255 respondents across 24 nations, including 138 women (54.1%), 169 physicians (60%), 32 scientists (11%), 20 educators (7%), and 18 public health professionals (6%). Overall, 37.4% indicated difficulties using PC research reports "50% or more of the time." The most common problems were synthesizing findings (58%) and assessing generalizability (42%). Difficulty was reported by 49% for qualitative, 46% for mixed methods, and 38% for observational research. Most users wanted richer reporting of theoretical foundation (53.7%); teams, roles, and organization of care (53.4%); and patient involvement in the research process (52.7%). Few reported difficulties with ethics or disclosure of funding or conflicts. Free-text answers described special challenges in reporting PC research: context of clinical care and setting; practical details of interventions; patient-clinician and team relationships; and generalizability, applicability and impact in the great variety of PC settings. Cross-check showed that few current reporting guidelines focus on these needs. CONCLUSIONS: Opportunities exist to improve the reporting of PC research to make it more useful for its many users, suggesting a role for a PC research reporting guideline.
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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 | 0.255 | 0.462 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".