Deliberate practice as a framework for evaluating feedback in residency training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it