Improving Communication Skills: Feedback from Faculty and Residents
Bibliographic record
Abstract
Objectives: To determine common themes in faculty and peer feedback for emergency medicine (EM) resident oral presentations. Methods: From January to July 2005, all EM residents received written feedback on communication skills from two faculty and two peer reviewers. The feedback forms were analyzed by using formal grounded theory. The two investigators independently reviewed 25% of the forms to generate a code of general categories and specific qualifiers. The independent codes were merged by consensus into a common code. All forms were independently coded by the two investigators using the common code. Coding disagreements were resolved by consensus, yielding a uniform inventory of feedback categories and qualifiers. Results: Twenty-one of 23 residents participated. Three hundred seventy-two data points were coded, with an interrater agreement of 85.7%. The five most common feedback themes were as follows: focus on key and relevant teaching points; increase audience participation; encourage higher level thinking; decrease content per slide; and add missing content relevant to teaching point. Peers differed from faculty, mentioning more frequently encourage higher level thinking and add missing content relevant to teaching point. Faculty differed from peers, mentioning increase clarity of teaching point and be engaging and enthusiastic more frequently. The difference in distribution of themes between faculty and peers was significant (χ2= 59.692; p = 0.01). Conclusions: The authors present a model for providing feedback to EM residents on communication skills that is individualized, behavior based, and includes peer comments. Faculty and peers differ in their recommendations. The findings may inform communication skills curricula for EM residents.
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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 | 0.028 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".