Does the format residents use to give and receive feedback about teaching affect the usefulness of the feedback?
Bibliographic record
Abstract
Purpose: An important element in each teaching workshop for resident doctors at the University of Saskatchewan is the microteaching sessions, including feedback. We set out to test our observations that one condition for organizing the feedback increased the quality of feedback. In one condition, residents provide and receive feedback in all areas listed on our feedback form; while in the other condition, they provide and receive feedback in some areas. Methods: Over 115 residents participated in the teaching workshop in the 2019-2020 academic year. Each resident experienced both conditions for giving and receiving feedback—about half with one condition first and the other half in the opposite order. We developed and tested a simple survey that asked about the usefulness of the feedback. Results: We used the Mann-Whitney U test for differences between some areas or all areas. We found a statistically significant difference with small to moderate effect sizes (Cohen’s d) favouring the some areas condition. Conclusion: Residents found the usefulness of feedback given or received using the feedback condition in some areas greater than all areas. We will now only use the some areas condition and recommend that other teaching workshops that use microteaching practice sessions consider using this condition.
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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 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".