Barriers and facilitators to recruitment of physicians and practices for primary care health services research at one centre
Bibliographic record
Abstract
BACKGROUND: While some research has been conducted examining recruitment methods to engage physicians and practices in primary care research, further research is needed on recruitment methodology as it remains a recurrent challenge and plays a crucial role in primary care research. This paper reviews recruitment strategies, common challenges, and innovative practices from five recent primary care health services research studies in Ontario, Canada. METHODS: We used mixed qualitative and quantitative methods to gather data from investigators and/or project staff from five research teams. Team members were interviewed and asked to fill out a brief survey on recruitment methods, results, and challenges encountered during a recent or ongoing project involving primary care practices or physicians. Data analysis included qualitative analysis of interview notes and descriptive statistics generated for each study. RESULTS: Recruitment rates varied markedly across the projects despite similar initial strategies. Common challenges and creative solutions were reported by many of the research teams, including building a sampling frame, developing front-office rapport, adapting recruitment strategies, promoting buy-in and interest in the research question, and training a staff recruiter. CONCLUSIONS: Investigators must continue to find effective ways of reaching and involving diverse and representative samples of primary care providers and practices by building personal connections with, and buy-in from, potential participants. Flexible recruitment strategies and an understanding of the needs and interests of potential participants may also facilitate recruitment.
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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 | 0.267 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".