How Do Surgery Students Use Written Language to Say What They See? A Framework to Understand Medical Students’ Written Evaluations of Their Teachers
Bibliographic record
Abstract
BACKGROUND: There remains debate regarding the value of the written comments that medical students are traditionally asked to provide to evaluate the teaching they receive. The purpose of this study was to examine written teaching evaluations to understand how medical students conceptualize teachers' behaviors and performance. METHOD: All written comments collected from medical students about teachers in the two surgery clerkships at the University of Alberta in 2009-2010 and 2010-2011 were collated and anonymized. A grounded theory approach was used for analysis, with iterative reading and open coding to identify recurring themes. A framework capturing variations observed in the data was generated until data saturation was achieved. Domains and subdomains were named using an in situ coding approach. RESULTS: The conceptual framework contained three main domains: "Physician as Teacher," "Physician as Person," and "Physician as Physician." Under "Physician as Teacher," students commented on specific acts of teaching and subjective perceptions of an educator's teaching values. Under the "Physician as Physician" domain, students commented on elements of their educator's physicianship, including communication and collaborative skills, medical expertise, professionalism, and role modeling. Under "Physician as Person," students commented on how both positive and negative personality traits impacted their learning. CONCLUSIONS: This framework describes how medical students perceive their teachers and how they use written language to attach meaning to the behaviors they observe. Such a framework can be used to help students provide more constructive feedback to teachers and to assist in faculty development efforts aimed at improving teaching performance.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".