Professional Responsibilities and Personal Impacts: Residents’ Experiences as Participants in Education Research
Bibliographic record
Abstract
PURPOSE: Although the field of medical education research is growing and residents are increasingly recruited to participate as subjects in research studies, little is known about their experiences. The goal of this study was to explore the experiences and perceptions of residents who are study participants in medical education research. METHOD: A phenomenographic approach was chosen to examine the range of residents' experiences as research participants. A maximum variation sampling strategy was used to identify residents with diverse experiences. Semistructured interviews that explored experiences as research participants were conducted with 19 residents in internal medicine, general surgery, and pediatrics at the University of Toronto in 2015-2016. RESULTS: The perceptions and experiences of participants fell into two categories. First, participation was seen as a professional responsibility to advance the profession, including a desire to improve future educational practices and a sense of responsibility to contribute to the academic cause. Second, the experience was noted for its personal impact, including benefits (e.g., receiving monetary incentives or novel educational experiences) and risks (e.g., coercion and breaches of confidentiality). The time required to participate in a study was identified as one of the most important factors affecting willingness to participate and the impact of participation. CONCLUSIONS: Being a participant in medical education research can be perceived in different ways. Understanding the view of resident participants is important to optimize potential benefits and minimize risks and negative consequences for them, thus fostering ready participation and high-quality research.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".