What Do Emergency Medicine Learners Want from Their Teachers? A Multicenter Focus Group Analysis
Bibliographic record
Abstract
BACKGROUND: To the best of the authors' knowledge, there are no reports describing what learners believe are good emergency medicine (EM) teaching practices. EM faculty developers are compromised by this lack of knowledge about what EM learners appreciate in their teachers. OBJECTIVES: To determine what Canadian EM learners consider to be good prerequisites and strategies for effective teaching in the emergency department (ED). METHODS: Clinical clerks and residents from the Canadian College of Family Physicians, Emergency Medicine certification [CCFP(EM)] fellowship program, the Royal College of Physicians and Surgeons of Canada, Emergency Medicine certification [FRCP(EM)] fellowship program, and off-service programs from all five Ontario medical schools participated in monitored focus-group sessions. Conversations were recorded, transcribed by a third party, and coded by two independent assessors using standard grounded theory methods. The text was categorized based on the final code into basic themes and specific qualifiers, which were then sorted by frequency of mention in the focus groups. Results are presented in descriptive fashion. RESULTS: Twenty-eight learners participated. They identified 14 major principles for good EM teaching, and a further 30 specific qualifiers. The top five principles were: "has a positive teacher attitude," "takes time to teach," "uses teachable moments well," "tailors teaching to the learner," and "gives appropriate feedback." Agreement on classification of ideas was 86%. CONCLUSIONS: Learners are sensitive to the constraints of the ED teaching environment, and have consistent views about good ED teaching practices. Among 14 general principles identified, "takes time to teach," "gives feedback," "tailors teaching to the learner," "uses teachable moments," and "has a good teacher attitude" were the most commonly reported.
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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".