Effectiveness of monetary incentives to recruit family physicians as study subjects: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Recruiting family physicians to participate as subjects of clinical studies is challenging. Monetary incentives are often used to increase enrolment, but few studies have measured the impact of doing so. As part of a trial seeking to compare two formats of interactive activities within an online continuing medical education (CME) program, we compared family physicians' recruitment rates with and without a monetary incentive. Recruitment took place by email. METHODS: Family physicians listed in the directory of the College of Physicians of the Province of Quebec (Canada) were emailed a one-page letter inviting them to participate in a randomized trial designed to evaluate a three-hour online CME program on rheumatology. Half of physicians were randomly allocated to receive a version of the letter that offered them $300 to participate (incentive group); the other half was not offered compensation (no-incentive group). RESULTS: A total of 1314 (91%) physicians had a valid email address as listed in the directory. The response rate was 7.5% (54/724) in the incentive group and 2.6% (19/724) in the no-incentive group (absolute difference [AD] 4.8%, 95% confidence interval [95% CI] = 2.6 - 7.2%; risk ratio [RR] 2.8, 95% CI = 1.7 - 4.7). Recruitment rates were 3.5% (25/724) in the incentive group and 0.6% (4/724) in the no-incentive group (AD 2.9%, 95% CI = 1.5 - 4.5%; RR 6.3, 95% CI = 2.2 - 17.9). CONCLUSIONS: Monetary incentives significantly increased recruitment, which nonetheless remained low. To reach recruitment targets, researchers are advised to plan for an extensive list of email contacts and to minimize restrictive eligibility criteria.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".