Medical student strategies for actively negotiating hierarchy in the clinical environment
Bibliographic record
Abstract
CONTEXT: Medical learning takes place in an extremely hierarchical environment. Medical students may struggle to understand how to succeed in such a rule-bound environment that leaves them vulnerable to the influences of social power. This study explores how medical students experience the clinical learning environment from their low-status positions in the social hierarchy. METHODS: Using constructivist grounded theory, we collected 88 hours of observation and 13 interviews with medical students completing clinical clerkships. Data collection focused on students' interactions with their supervisors, colleagues and other staff members as they completed the core rotations of their clinical clerkships. Data analysts used a constant comparative approach to remain alert to the different ways in which medical students experienced and responded to social power used by their supervisors and colleagues. RESULTS: We describe a cyclical theory of how medical students appraised the environment, the needs and preferences of their supervisors and their personal resources in order to select and enact a strategy for interacting. They used these strategies when in the presence of supervisors, but also when supervisors were absent in preparation for the next interaction. The ways in which medical students chose and employed these strategies reflect a significant use of social and cognitive resources. CONCLUSIONS: Power is an important component of the social culture of clinical learning environments; understanding the ways in which medical students experience and react to power can help educators, learners and administrators optimise learning opportunities. Medical education increasingly encourages students to exercise agency in seeking feedback and directing their own learning; this may be particularly challenging for students who cannot interpret social cues well, and those who lack social capital.
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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 | 0.006 | 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 | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".