Developing Residents as Medical Educators via the McMaster Multidisciplinary Academic Day Planning Committee
Bibliographic record
Abstract
Background Residents are being asked to perform educator roles such as curriculum design and learner assessment with minimal professional development in leadership or medical education. The Multidisciplinary Academic Day (MAD) planning committee is a resident-led initiative responsible for delivering combined educational half-day sessions and workshops for all residents at McMaster University. Objective We sought to determine the impact participation in MAD planning committee had on residents' professional development and career goals. Methods We conducted a program evaluation survey of 19 of 30 (63.3%) current and former committee members to determine how the MAD planning committee's alumni perceived its usefulness, and how participation affected their professional development. Results Residents cited a desire to gain medical education experience, learn about event planning and management, and improve resident education as reasons for joining the committee; 89.5% of respondents felt they had met these goals. Experience on the committee included skills related to curriculum design, developing needs assessments and programmatic evaluation. Many residents felt it helped them acquire leadership skills such as decision-making, idea generation, delegation, and public speaking. Several noted that it had sparked an interest in medical education as part of an academic career, and one former member subsequently pursued a Master's of Education. The majority of the respondents (78.9%) felt it was helpful for their careers and 94.7% would recommend this experience to others interested in leadership and medical education. Conclusion Involvement in the MAD planning committee is a highly useful way for residents to acquire leadership skills, develop an interest in medical education and work in a multidisciplinary team.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".