Residents as Research Subjects: Balancing Resident Education and Contribution to Advancing Educational Innovations
Bibliographic record
Abstract
Background: Research in education advances knowledge and improves learning, but the literature does not define how to protect residents' rights as subjects in studies or how to limit the impact of their participation on their clinical training. Objective: We aimed to develop a consensual framework on how to include residents as participants in education research, with the dual goal of protecting their rights and promoting their contributions to research. Methods: A nominal group technique approach was used to structure 3 iterative meetings held with the pre-existing residency training program committee and 7 invited experts between September 2018 and April 2019. Thematic text analysis was conducted to prepare a final report, including recommendations. Results: Five themes, each with recommendations, were identified: (1) Freedom of participation: participation, non-participation, or withdrawal from a study should not interfere with teacher-learner relationship (recommendation: improve recruitment and consent forms); (2) Avoidance of over-solicitation (recommendation: limit the number of ongoing studies); (3) Management of time dedicated to participation in research (recommendations: schedule and proportion of time for study participation); (4) Emotional safety (recommendation: requirement for debriefing and confidential counseling); and (5) Educational safety: data collected during a study should not influence clinical assessment of the resident (recommendation: principal investigator should not be involved in the evaluation process of learners in clinical rotation). Conclusions: Our nominal group technique approach resulted in raising 5 specific issues about freedom of participation of residents in research in medical education, over-solicitation, time dedicated to research, emotional safety, and educational safety.
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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 | 0.254 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".