A Systematic Process for Recruiting Physician-Patient Dyads in Practice-based Research Networks (PBRNs)
Bibliographic record
Abstract
BACKGROUND: Recruiting physicians and patients for primary care research is difficult, and low participation can greatly affect the validity of research. While practice-based research networks (PBRNs) offer advantages of scale for recruitment, the barriers are perennial. We designed a systematic process for recruiting physician-patient dyads in PBRNs and tested it in EXACKTE2, a large, cross-sectional, dyadic study. METHODS: Based on known barriers, we designed a systematic process for recruiting dyads of family physicians and their patients and implemented it in 2 primary care practice-based research networks in Canada: one in Ontario (11 practices) and one in Quebec (6 practices). Dyads (one physician with one patient) were recruited simultaneously to explore their mutual influence during consultations. A key element of the process was a research assistant assigned to each practice. This person closely accompanied the recruitment process, liaising with staff and taking charge of interviews, questionnaires, and follow-up. RESULTS: In total, 276 physicians and patients were recruited in 17 primary care practices in 2 primary care networks in Ontario and Quebec, representing a participation rate of more than 72% of eligible physicians and more than 64% of eligible patients. CONCLUSION: We established a systematic process to conduct successful dyadic recruitment of physicians and patients in PBRNs.
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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 | 0.368 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".