Feedback from health professionals in postgraduate medical education: Influence of interprofessional relationship, identity and power
Bibliographic record
Abstract
INTRODUCTION: Capitalising on direct workplace observations of residents by interprofessional team members might be an effective strategy to promote formative feedback in postgraduate medical education. To better understand how interprofessional feedback is conceived, delivered, received and used, we explored both feedback provider and receiver perceptions of workplace feedback. METHODS: We conducted 17 individual interviews with residents and eight focus groups with health professionals (HPs) (two nurses, two rehabilitation therapists, two pharmacists and two social workers), for a total of 61 participants. Using a constructivist grounded theory approach, data collection and analysis proceeded as an iterative process using constant comparison to identify and explore themes. RESULTS: Conceptualisations and content of feedback were dependent on whether the resident was perceived as a learner or a peer within the interprofessional relationship. Residents relied on interprofessional role understanding to determine how physician competencies align with HP roles. The perceived alignment was unique to each profession and influenced feedback credibility judgements. Residents prioritised feedback from physicians or within the Medical Expertise domain-a role that HPs felt was over-valued. Despite ideal opportunities for direct observation, operational enactment of feedback was influenced by power differentials between the professions. DISCUSSION: Our results illuminate HPs' conceptualisation of feedback for residents and the social constructs influencing how their feedback is disseminated. Professional identity and social categorisation added complexity to feedback acceptance and incorporation. To ensure that interprofessional feedback can achieve desired outcomes, education programmes should implement strategies to help mitigate intergroup bias and power imbalance.
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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 | 0.020 | 0.080 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".