Supervising the senior medical resident: Entrusting the role, supporting the tasks
Bibliographic record
Abstract
INTRODUCTION: Postgraduate competency-based medical education has been implemented with programmatic assessment that relies on entrustment-based ratings. Yet, in less procedurally oriented specialties such as internal medicine, the relationship between entrustment and supervision remains unclear. We undertook the current study to address how internal medicine supervisors conceptualise entrusting senior medical residents while supervising them on the acute care wards. METHODS: Guided by constructivist grounded theory, we interviewed 19 physicians who regularly supervised senior internal medicine residents on inpatient wards at three Canadian universities. We developed a theoretical model through iterative cycles of data collection and analysis using a constant comparative process. RESULTS: On the internal medicine ward, the senior resident role is viewed as a fundamentally managerial and rudimentary version of the supervisor's role. Supervisors come to trust their residents in the senior role through an early 'hands-on' period of assessment followed by a gradual withdrawal of support to promote independence. When considering entrustment, supervisors focused on entrusting a particular scope of the senior resident role as opposed to entrustment of individual tasks. Irrespective of the scope of the role that was entrusted, supervisors at times stepped in and stepped back to support specific tasks. CONCLUSION: Supervisors' stepping in and stepping back to support individual tasks on the acute care ward has an inconsistent relationship to their entrustment of the resident with a particular scope of the senior resident role. In this context, entrustment-based assessment would need to capture more of the holistic perspective of the supervisor's entrustment of the senior resident role. Understanding the dance of supervision, from relatively static overall support of the resident in their role, to fluidly stepping in and out for specific patient care tasks, allows us insight into the affordances of the supervisory relationship and how it may be leveraged for assessment.
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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".