The Case for Feedback-in-Practice as a Topic of Educational Scholarship
Bibliographic record
Abstract
The importance of clinical performance feedback is well established and the factors relevant to its effectiveness widely recognized, yet feedback continues to play out in problematic ways. For example, learning culture modifications shown to facilitate feedback have not seen widespread adoption, and the learner-educator interactions prescribed by research rarely occur organically. Nevertheless, medical learners achieve clinical competence, suggesting a need to expand educational scholarship on this topic to better account for learner growth. This Scholarly Perspective argues for a more extensive exploration of feedback as an educational activity embedded in clinical practice , where joint clinical work that involves an educator and learner provides a locus for feedback in the midst of performance. In these clinically embedded feedback episodes, learning and performance goals are constrained by the task at hand, and the educator guides the learner in collaboratively identifying problematic elements, naming and reframing the source of challenge, and extrapolating implications for further action. In jointly conducting clinical tasks, educators and learners may frequently engage in feedback interactions that are both aligned with workplace realities and consistent with current theoretical understanding of what feedback is. However, feedback embedded in practice may be challenged by personal, social, and organizational factors that affect learners' participation in workplace activity. This Scholarly Perspective aims to provide a conceptual framework that helps educators and learners be more intentional about and fully participatory in this important educational activity. By topicalizing this feedback-in-practice and exploring its integration with the more commonly foregrounded feedback-on-practice , future educational scholarship may achieve optimal benefit to learners, educators, and clinical practice.
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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 | 0.112 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.016 | 0.161 |
| Scholarly communication | 0.043 | 0.072 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.032 | 0.039 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".