Faculty Perceptions on the Roles of Mentoring, Advising, and Coaching in an Anesthesiology Residency Program: Mixed Methods Study
Bibliographic record
Abstract
Background: Mentoring, advising, and coaching are essential components of resident education and professional development. Despite their importance, there is limited literature exploring how anesthesiology faculty perceive these practices and their role in supporting residents. Objective: This study aims to investigate anesthesiology faculty perspectives on the significance, implantation strategies, and challenges associated with mentorship, advising, and coaching in resident education. Methods: A comprehensive survey was administrated to 93 anesthesiology faculty members at Washington University School of Medicine. The survey incorporated quantitative Likert-scale questions and qualitative short-answer responses to assess faculty perceptions of the value, preferred formats, essential skills, and capacity for fulfilling multiple roles in these support practices. Additional areas of focus included the impact of staffing shortages, training requirements, and the potential of these practices to enhance faculty recruitment and retention. Results: The response rate was 44% (n=41). Mentoring was identified as the most important aspect, with 88% (n=36) of faculty respondents indicating its significance, followed by coaching, which was highlighted by 78% (n=32) of respondents. The majority felt 1 faculty member can effectively hold multiple roles for a given trainee. The respondents desired additional training for roles and found roles to be rewarding. All roles were seen as facilitating recruitment and retention. Barriers included faculty burnout; confusion between roles; time constraints; and desire for specialized training, especially in coaching skills. Conclusions: Implementing structured mentoring, advising, and coaching can profoundly impact resident education but requires role clarity, protected time, culture change, leadership buy-in, and faculty development. Targeted training and operational investments could enable programs to actualize immense benefits from high-quality resident support modalities. Respondents emphasized that resident needs evolve over time, necessitating flexibility in appropriate faculty guidance. While coaching demands unique skills, advising hinges on expertise and mentoring depends on relationship-building. Systematic frameworks of coaching, mentoring, and advising programs could unlock immense potential. However, realizing this vision demands surmounting barriers such as burnout, productivity pressures, confusion about logistics, and culture change. Ultimately, prioritizing resident support through high-quality personalized guidance can recenter graduate medical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".