Learning Conversations: An Analysis of the Theoretical Roots and Their Manifestations of Feedback and Debriefing in Medical Education
Bibliographic record
Abstract
Feedback and debriefing are experience-informed dialogues upon which experiential models of learning often depend. Efforts to understand each have largely been independent of each other, thus splitting them into potentially problematic and less productive factions. Given their shared purpose of improving future performance, the authors asked whether efforts to understand these dialogues are, for theoretical and pragmatic reasons, best advanced by keeping these concepts unique or whether some unifying conceptual framework could better support educational contributions and advancements in medical education.The authors identified seminal works and foundational concepts to formulate a purposeful review and analysis exploring these dialogues' theoretical roots and their manifestations. They considered conceptual and theoretical details within and across feedback and debriefing literatures and traced developmental paths to discover underlying and foundational conceptual approaches and theoretical similarities and differences.Findings suggest that each of these strategies was derived from distinct theoretical roots, leading to variations in how they have been studied, advanced, and enacted; both now draw on multiple (often similar) educational theories, also positioning themselves as ways of operationalizing similar educational frameworks. Considerable commonality now exists; those studying and advancing feedback and debriefing are leveraging similar cognitive and social theories to refine and structure their approaches. As such, there may be room to merge these educational strategies as learning conversations because of their conceptual and theoretical consistency. Future scholarly work should further delineate the theoretical, educational, and practical relevance of integrating feedback and debriefing.
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".