Entrustment of the on-call senior medical resident role: implications for patient safety and collective care
Bibliographic record
Abstract
BACKGROUND: The on-call responsibilities of a senior medicine resident (SMR) may include the admission transition of patient care on medical teaching teams (MTT), supervision of junior trainees, and ensuring patient safety. In many institutions, there is no standardised assessment of SMR competency prior to granting these on-call responsibilities in internal medicine. In order to fulfill competency based medical education requirements, training programs need to develop assessment approaches to make and defend such entrustment decisions. The purpose of this study is to understand the clinical activities and outcomes of the on-call SMR role and provide training programs with a rigorous model for entrustment decisions for this role. METHODS: This four phase study utilizes a constructivist grounded theory approach to collect and analyse the following data sets: case study, focus groups, literature synthesis of supervisory practices and return-of-findings focus groups. The study was conducted in two Academic Health Sciences Centres in Ontario, Canada. The case study included ten attending physicians, 13 SMRs, 19 first year residents and 14 medical students. The focus groups included 19 SMRs. The later, return-of-findings focus groups included ten SMRs. RESULTS: Five core on-call supervisory tasks (overseeing ongoing patient care, briefing, case review, documentation and preparing for handover) were identified, as well as a range of practices associated with these tasks. We also identified challenges that influenced the extent to which SMRs were able to effectively perform the core tasks. At times, these challenges led to omissions of the core tasks and potentially compromised patient safety and the admission transition of care. CONCLUSION: By identifying the core supervisory tasks and associated practices, we were able to identify the competencies for the on-call SMR role. Our findings can further be used by training programs for assessment and for making entrustment decisions.
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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 | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".