What Faculty Want: Academic and Community Emergency Physicians’ Perceptions of Learner Feedback
Bibliographic record
Abstract
Introduction Faculty development is often deployed by central medical schools, with little guidance from end-users. How and what faculty members can use to improve their performance requires a deeper understanding from this user group. This study aims to explore how faculty perceive learners' feedback about their performance as educators. Methods This study is an explanatory mixed-method research, wherein community- and academic-based emergency medicine faculty members from nine regional hospitals were surveyed about their perceptions of various outcome measures for faculty development. Selected participants were invited to follow-up interviews. We analyzed the physicians' perceptions toward teaching and performance feedback data based on faculty's gender, role as academic or community physician, and work experience. Results The quantitative phase has 104 participants, and 15 of these were followed up with interviews. The gender of faculty does not have statistical or practical differences regarding their perceptions of learner feedback. Type of practice contains meaningful insights about the perception of learner feedback although it does not have a statistical difference. Moreover, an inverse trend exists between the physicians' years of experience and their perceived value of learner feedback. Kruskal-Wallis test showed a significant difference in the faculty's experience level and their perceived value for the metric "quantity of feedback commentary compared to their peer group" (H(4) = 12.21, p = 0.02), specifically between junior and senior faculty (p = 0.007). Some faculty stated that experienced faculty may perceive they have a very well-established style. Conclusions Diversifying feedback sources and delivery may be useful for different groups of faculty members. Junior physicians are more interested in gaining feedback about the quantity of their written feedback to students compared to more senior physicians. Learner feedback holds promise to trigger continuous improvement in community sites for those who fall behind compared to the academic sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".