Feedback and the educational alliance: examining credibility judgements and their consequences
Bibliographic record
Abstract
CONTEXT: Several recent studies have documented the fact that, in considering feedback, learners are actively making credibility judgements about the feedback and its source. Yet few have intentionally explored such judgements to gain a deeper understanding of how the process works or how these judgements might interact to influence engagement with and interpretation of feedback. Using the educational alliance framework, we sought to elaborate an understanding of learners' credibility judgements and their consequences. METHODS: Using constructivist grounded theory we conducted semi-structured interviews with psychiatry residents. We used a theoretical sampling approach that invited participants with diverse scores based on a previously published feedback survey and an investigator-developed educational alliance inventory. Consistent with the principles of grounded theory analysis, data were collected and analysed in an iterative process to identify themes. RESULTS: Participants depicted themselves as actively contemplating feedback and considering it thoughtfully in light of complex judgements regarding their supervisor, the relationship with their supervisor and the larger context in which the feedback interactions were occurring. These judgements focused on the supervisor's credibility both as a clinician and as a partner in the educational alliance. The educational alliance is judged by trainees in relation to the supervisor's engagement as an educator, commitment to promoting growth of residents and positive attitude toward them. CONCLUSIONS: Our findings suggest that credibility is a multifaceted judgement that occurs not only at the moment of the feedback interaction but early in and throughout an educational relationship. It not only affects a learner's engagement with a particular piece of feedback at the moment of delivery, but also has consequences for future engagement with (or avoidance of) further learning interactions with the supervisor. These findings can help medical educators develop a more meaningful understanding of the context in which feedback takes place.
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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 | 0.104 | 0.478 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".