Alienation in the Teaching Hospital: How Physician Non-Greeting Behaviour Impacts Medical Students’ Learning and Professional Identity Formation
Bibliographic record
Abstract
Introduction: Clinical workplaces offer unrivalled learning opportunities if students get pedagogic and affective support that enables them to confidently participate and learn from clinical activities. If physicians do not greet new students, the learners are deprived of signals of social respect and inclusion. This study explored how physicians’ non-greeting behaviour may impact medical students’ participation, learning, and professional identity formation in clinical placements. Methods: We analysed 16 senior Norwegian medical students’ accounts of non-greeting behaviours among their physician supervisors in a reflexive thematic analysis of focus group interview data. Results: The main themes were: A) Descriptions of non-greeting. Not being met with conduct signalling rapport, such as eye contact, saying hello, using names, or introducing students at the workplace, was perceived as non-greeting, and occurred across clinical learning contexts. B) Effects on workplace integration. Non-greeting was experienced as a rejection that hurt students’ social confidence, created distance from the physician group, and could cause avoidance of certain workplace activities or specific medical specialties. C) Impact on learning. Non-greeting triggered avoidance and passivity, reluctance to ask questions or seek help or feedback, and doubts about their suitability for a medical career. Conclusion: Medical students’ accounts of being ignored or treated with disdain by physician superiors upon entering the workplace suggest that unintended depersonalising behaviour is ingrained in medical culture. Interaction rituals like brief eye contact, a nod, a “hello”, or use of the student’s name, can provide essential affective support that helps medical students thrive and learn in the clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".