Optimizing feedback reception: a scoping review of skills and strategies for medical learners
Bibliographic record
Abstract
Background: Feedback remains essential to a learner’s professional development. Most feedback literature focuses on provision of feedback, and there is a lack of evidence-based data to support learners in developing skills to receive, evaluate and use feedback, independently of context. This scoping review mapped the literature regarding strategies and skills that optimize medical learners’ reception to feedback. Methods: Investigators conducted searches in MEDLINE, Embase, ERIC, APA PsycINFO and Web of Science Core collection from inception to May 2023. Study inclusion criteria were primary evidence sources, and strategies or skills for improved feedback reception for medical learners. Data were screened and extracted by pairs of independent reviewers. Investigators summarized study characteristics, outcomes, educational methods, and interventions. Results: Of 7692 total studies, six provided strategies and skills to improve feedback reception. Delivery of education was via workshops (n = 5 studies) that proposed cognitive, reflective and experiential learning activities, all reporting learners’ self-perceived improvement of feedback behaviour. Nine strategies and seven tools were identified, focusing on general approach, soliciting or evaluating feedback. Conclusion: The six included studies outline nine strategies and seven skills for improved learner feedback reception, focusing on overall approach and agentic behaviours without evaluation of the strategies or skills in practice. Key concepts and gaps in the literature were identified and may guide further investigation to optimize learner reception to feedback.
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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".