Striking a Balance: A Qualitative Thematic Analysis of General Surgery Residents’ Perceptions of their Role as Medical Student Educators
Bibliographic record
Abstract
BACKGROUND: General surgery (GS) residents are critical educators for medical students (MS). However, GS resident perceptions of this role remain unclear. This study aims to characterize GS resident perspectives on their roles as MS teachers. METHODS: We conducted semi-structured interviews with GS residents at a single institution. We designed open-ended questions to explore resident teaching roles: focusing on their definitions, support received, perceived importance, motivations, and barriers to teaching. Interview transcripts were inductively analyzed using thematic analysis, with iterative coding conducted by multiple team members to identify and refine emergent themes. RESULTS: A total of 19 residents participated (postgraduate year range: 1-7). Residents had varied definitions of their role as teachers, frequently emphasizing MS identity formation and comfort within the surgical environment over technical knowledge transfer. Most residents expressed a sense of obligation to teach, though this was often self-driven rather than incentivized. Engaging with MS was viewed as a significant daily responsibility, considered almost as important as patient care. While no structured curriculum for teaching exists, residents emphasized the value of exemplary role models in shaping their teaching approach and the desire for recognition of their teaching efforts. Challenges to teaching included workflow and patient care demands, with confidence in clinical and technical skills often impacting teaching engagement. CONCLUSIONS: Our proposed framework demonstrates that GS residents experience teaching MS as a dynamic balance of multiple professional roles and responsibilities. They view themselves as mentors and cultural liaisons, along with educators. However, confidence varies in balancing dual roles as learners and teachers. Understanding these perceptions can guide institutions in better supporting GS residents as educators.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".