Twelve tips to guide effective participant recruitment for interprofessional education research
Bibliographic record
Abstract
BACKGROUND: The success of research in interprofessional education is largely due to the participation of students. Their recruitment is, however, perhaps the most challenging part of any study, and, yet, is a key determinant of the results. AIM: The aim of this article is to provide a "how to guide" for medical education researchers to facilitate the recruitment of students across health professions. RESULTS: The 12 tips are (1) establish clear expectations with your research team from the start; (2) do your homework: invest time and energy in pre-recruitment preparation; (3) develop a plan: be realistic about your resources; (4) create a "Buzz" about your interprofessional research; (5) prepare multiple communication methods - can't just rely on one! (6) engage volunteers across professions to participate; (7) address the participant's willingness to take part in the research; (8) demonstrate good interpersonal skills; (9) be diligent in tracking participants; (10) show appreciation and share results; (11) consider participant incentives: are they really important? (12) maintain tenacity - no one said interprofessional recruitment was easy! CONCLUSIONS: Interprofessional studies offer numerous logistical, administrative and scheduling challenges; the 12 tips are provided to help medical education researchers develop and manage the successful recruitment of students across the health professions.
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How this classification was reachedexpand
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads 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".