Deliberate practice as a framework for evaluating feedback in residency training
Bibliographic record
Abstract
OBJECTIVE: Using the theory of deliberate practice, a key component of Ericsson's theory of expertise development, this study aims to evaluate the quality of written feedback given to learners. METHODS: The authors created a feedback scoring system based on the key elements of deliberate practice and used it to assess the quality of written feedback provided to residents in 205 mini-CEX encounter forms. Scores were assigned to each feedback entry for identification of the following: Task, performance gap and action plan. RESULTS: The scoring system allowed for reliable identification of the components that facilitate deliberate practice in written feedback provided to trainees. However, only one of these components was identified in 70% of the feedback entries. A specific task was identified in 56%, whereas specific performance gaps and action plans were identified in only 3.9% and 13.7% of encounters, respectively. CONCLUSIONS: Scoring written feedback identified that tasks were often specifically described, but performance gaps and action plans were less frequently and specifically mentioned. Educators might improve feedback effectiveness by better articulating to trainees the gap between their performance and an expert standard, as well as by providing them with specific learning plans.
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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 | 0.129 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".