Clinician Use of Primary Care Research Reports
Bibliographic record
Abstract
PURPOSE: To assess how primary care practitioners use reports of general health care (GHC) and primary care (PC) research and how well reports deliver what they need to inform clinical practice. METHODS: International, interprofessional online survey, 2019, of primary care clinicians who see patients at least half time. Respondents used frequency scales to report how often they access both GHC and PC research and how frequently reports meet needs. Free-text short comments recorded comments and suggestions. RESULTS: Survey yielded 252 respondents across 29 nations, 55% (121) women, including 88% (195) physicians, nurses 5% (11), and physician assistants 3% (7). Practitioners read research reports frequently but find they usually fail to meet their needs. For PC research, 33% (77) accessed original reports in academic journals weekly or daily, and 36% found reports meet needs "frequently" or "always." They access reports of GHC research slightly more often but find them somewhat less useful. CONCLUSIONS: PC practitioners access original research in academic journals frequently but find reports meet information needs less than half the time. PC research reflects the unique PC setting and so reporting has distinct focus, needs, and challenges. Practitioners desire improved reporting of study context, interventions, relationships, generalizability, and implementation.
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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 | 0.083 | 0.355 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".