Feedback Frequency in Competence by Design: A Quality Improvement Initiative
Bibliographic record
Abstract
BACKGROUND: Otolaryngology-head and neck surgery is in the first wave of residency training programs in Canada to adopt Competence by Design (CBD), a model of competency-based medical education. CBD is built on frequent, low-stakes assessments and requires an increase in the number of feedback interactions. The University of Toronto otolaryngology-head and neck surgery residents piloted the CBD model but were completing only 1 assessment every 4 weeks, which was insufficient to support CBD. OBJECTIVE: This project aimed to increase assessment completion to once per resident per week using quality improvement methodology. METHODS: Stakeholder engagement activities had residents and faculty characterize barriers to assessment completion. Brief electronic assessment forms were completed by faculty on residents' personal mobile devices in face-to-face encounters, and the number completed per resident was tracked for 10 months during the 2016-2017 pilot year. Response to the intervention was analyzed using statistical process control charts. RESULTS: The first bundled intervention-a rule set dictating which clinical instance should be assessed, combined with a weekly reminder implemented for 10 weeks-was unsuccessful in increasing the frequency of assessments. The second intervention was a leaderboard, designed on an audit-and-feedback system, which sent weekly comparison e-mails of each resident's completion rate to all residents and the program director. The leaderboard demonstrated significant improvement from baseline over 10 weeks, increasing the assessment completion rate from 0.22 to 2.87 assessments per resident per week. CONCLUSIONS: A resident-designed audit-and-feedback leaderboard system improved the frequency of CBD assessment completion.
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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 | 0.178 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".