The “Educational Alliance” as a Framework for Reconceptualizing Feedback in Medical Education
Bibliographic record
Abstract
Feedback has long been considered a vital component of training in the health professions. Nonetheless, it remains difficult to enact the feedback process effectively. In part, this may be because, historically, feedback has been framed in the medical education literature as a unidirectional content-delivery process with a focus on ensuring the learner's acceptance of the content. Thus, proposed solutions have been organized around mechanistic, educator-driven, and behavior-based best practices. Recently, some authors have begun to highlight the role of context and relationship in the feedback process, but no theoretical frameworks have yet been suggested for understanding or exploring this relational construction of feedback in medical education. The psychotherapeutic concept of the "therapeutic alliance" may be valuable in this regard.In this article, the authors propose that by reorganizing constructions of feedback around an "educational alliance" framework, medical educators may be able to develop a more meaningful understanding of the context-and, in particular, the relationship-in which feedback functions. Use of this framework may also help to reorient discussions of the feedback process from effective delivery and acceptance to negotiation in the environment of a supportive educational relationship.To explore and elaborate these issues and ideas, the authors review the medical education literature to excavate historical and evolving constructions of feedback in the field, review the origins of the therapeutic alliance and its demonstrated utility for psychotherapy practice, and consider implications regarding learners' perceptions of the supervisory relationship as a significant influence on feedback acceptance in medical education settings.
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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 | 0.041 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.012 | 0.077 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".