Model of Interactive Clinical Supervision in Acute Care Environments. Balancing Patient Care and Teaching
Bibliographic record
Abstract
RATIONALE: Progressive trainee autonomy is considered essential for clinical learning, but potentially harmful for patients. How clinical supervisors and medical trainees establish progressive levels of autonomy in acute care environments without compromising patient safety is largely unknown. OBJECTIVES: To explore how bedside interactions among supervisors and trainees relate to trainee involvement in patient care and to clinical oversight. METHODS: We conducted a qualitative study based on constructivist grounded theory methodology. We used participant observation for our data collection. We observed the overt teaching interactions among trainees and staff physicians in the critical care units of two university-affiliated hospitals during 74 acute care episodes. Our analysis led to the elaboration of a theoretical model of clinical supervision. MEASUREMENTS AND MAIN RESULTS: A model of interactive clinical supervision is proposed on the basis of three themes: engaging without enactment, sharing care with support, and caring independently with feedback. Each theme regroups different teaching interactions. Engaging in monologues and dialogues about patient care and facilitating hands-off care provision involved progressive levels of trainee involvement without risk for patients. Facilitating hands-on provision of care and providing support-in-action encouraged further trainee involvement with limited risks for patients. Providing feedback-on-action created additional learning opportunities based on trainee independent involvement in clinical activities. CONCLUSIONS: Engaging in teaching interactions during acute care episodes allows trainees to exercise progressive autonomy and supervisors to provide adequate clinical oversight. Our model of interactive clinical supervision can inform faculty development initiatives. Learning outcomes resulting from different levels of trainee autonomy should be further explored.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".