Feedback with feelings: the human complexity of expressing judgements about performance
Bibliographic record
Abstract
Feedback is an emotional business; evoking optimism, fear or disappointment, which in turn can lead to engagement, further feedback seeking or avoidance. Emotions therefore are not just background noise but are fundamental to the experience. Through conceptualising emotions as embodied, social and complex, we seek to better understand how feelings work within feedback in clinical education, beyond an individual 'managing' their emotions. In this post-qualitative study, we ask: How does the interplay between feelings and feedback unfold in specialty medical training? To this end, we conducted a focussed ethnography of feedback in intensive care medicine and surgical training in Australian tertiary-care hospitals. Thinking with theory, we traced how trainees' feelings move within and between feedback encounters through observation-based field notes and interview transcripts. We provide thick description of how feedback is saturated with feeling, integrating our findings with discussion. Supervisors expressed their judgements about trainee performance as feelings, through feelings and about feelings. And trainees responded with feelings of their own. Formal feedback particularly intensified feelings, which 'stuck with' trainees, leading to action, including avoidance. Feelings served to strengthen relationships and reinforce social hierarchies both within and beyond the supervisor-trainee dyad. We infer that the judgements made in and around feedback - such as appraisal of source credibility, assessment of performance quality, and deciding future actions - are themselves fundamentally entangled with feelings. A first step to remedy the desire to 'manage' emotions through 'putting them away' is to acknowledge their presence in clinical learning environments.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".