Beyond individualism: professional culture and its influence on feedback
Bibliographic record
Abstract
CONTEXT: Although feedback is widely considered essential to learning, its actual influence on learners is variable. Research on responsivity to feedback has tended to focus on individual rather than social or cultural influences on learning. In this study, we explored how feedback is handled within different professional cultures, and how the characteristics and values of a profession shape learners' responses to feedback. METHODS: Using a constructivist grounded theory approach, we conducted 12 focus groups and nine individual interviews (with a total of 50 participants) across three cultures of professional training in, respectively, music, teacher training and medicine. Constant comparative analysis for recurring themes was conducted iteratively. RESULTS: Each of the three professional cultures created a distinct context for learning that influenced how feedback was handled. Despite these contextual differences, credibility and constructiveness emerged as critical constants, identified by learners across cultures as essential for feedback to be perceived as meaningful. However, the definitions of credibility and constructiveness were distinct to each professional culture and the cultures varied considerably in how effectively they supported the occurrence of feedback with these critical characteristics. CONCLUSIONS: Professions define credibility and constructiveness in culturally specific ways and create contexts for learning that may either facilitate or constrain the provision of meaningful feedback. Comparison with other professional cultures may offer strategies for creating a productive feedback culture within medical education.
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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 | 0.022 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".