Incentives for recruiting trainee participants in medical education research
Bibliographic record
Abstract
INTRODUCTION: In the growing field of medical education research, participant recruitment can be challenging. Incentives, either tangible or intangible, may be offered to encourage participation. This study aimed to understand these incentives and explore the relationship between study quality and incentives in medical education research. METHODS: We reviewed research studies examining medical trainees published in five major journals in 2008. Tangible and intangible incentives used in recruitment were extracted by two researchers. For each quantitative article, medical education research quality instrument (MERSQI) score was calculated and citation counts for all articles were compiled. RESULTS: Of 215 included articles, 8% explicitly reported incentives. Tangible incentives (value range $15-$60 USD) were offered in 7.9% of studies. Intangible incentives were identified in 30% of studies but only one specifically discussed their use. Tangible incentives correlated with a higher MERSQI score (p < 0.001) and with citations (p < 0.001). CONCLUSION: Most studies in medical education did not describe incentives for participation. Information regarding incentives should be reported in all studies to help inform future recruitment efforts and also to understand the study context including factors that may influence participants motivation.
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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 | 0.127 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".