The Teacher, the Assessor, and the Patient Protector: A Conceptual Model Describing How Context Interfaces With the Supervisory Roles of Academic Emergency Physicians
Bibliographic record
Abstract
OBJECTIVES: Emergency medicine is a fast-paced specialty that demands emergency physicians to respond to rapidly evolving patient presentations, while engaging in clinical supervision. Most research on supervisory roles has focused on the behaviors of attending physicians, including their individual preferences of supervision and level of entrustment of clinical tasks to trainees. However, less research has investigated how the clinical context (patient case complexity, workflow) influences clinical supervision. In this study, we examined how the context of the emergency department (ED) shapes the ways in which emergency physicians reconcile their competing roles in patient care and clinical supervision to optimize learning and ensure patient safety. METHODS: Emergency physicians who regularly participated in clinical supervision in several academic teaching hospitals were individually interviewed using a semi-structured format. The interviews were transcribed and analyzed using a constructivist grounded theory approach. RESULTS: Sixteen emergency physicians were asked to reflect on their clinical supervisory roles in the ED. We conceptualized a model that describes three prominent roles: teacher, assessor, and patient protector. Contextual features such as trainee competence, pace of the ED, patient complexity, and the culture of academic medicine influenced the extent to which certain roles were considered salient at any given time. CONCLUSIONS: This conceptual model can inform researchers and medical educators about the role of context in accentuating or minimizing various roles of emergency physicians. Identifying how context interfaces with these roles may help design faculty development initiatives aimed to navigate the tension between urgent patient care and medical education for emergency physicians.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".