Barriers and facilitators to recruitment of physicians and practices for primary care health services research at one centre
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.141 | 0.360 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it