Working in the dead of night: exploring the transition to after‐hours duty
Bibliographic record
Abstract
CONTEXT: Transitions, although often difficult, represent integral components of medical training. New postgraduate trainees (first-year residents) find themselves in an especially challenging transition as they are expected to fulfil both learning and service expectations concurrently. Workplace learning theory has been suggested as a lens through which to understand this unique educational, yet service-oriented, role. This tension may be further amplified overnight when residents are on-call with little to no support. OBJECTIVES: The aims of this study were to explore the transition from medical student to resident with respect to the on-call experience, and to provide theory-based suggestions to enhance learning during this unique transition. METHODS: We conducted an interpretivist qualitative study by interviewing eight medical students and 10 first-year residents from six different specialty training programmes across four academic sites. Each semi-structured interview was transcribed verbatim and anonymised. Resident interview transcripts were initially coded for major themes, after which medical student interview transcripts were coded for consistencies and discrepancies. RESULTS: Four interrelated themes were identified in students' and residents' descriptions of on-call experiences: (i) shift in responsibility; (ii) supervisory support; (iii) contextual conditions, and (iv) clarity of expectations. Generally, students were not able to anticipate the challenges they would face as residents on-call, and residents perceived the transition as sudden with little emphasis placed on learning. CONCLUSIONS: First-year residents face multiple challenges during on-call, which may prevent optimal learning in this setting. These challenges are amplified by the large gap between the respective roles of medical students and residents. We identified promoters of and barriers to effective learning in this environment and, by using workplace learning theory, provide recommendations for how we might be able to enhance medical students' preparation for and first-year residents' learning during experiences of being on-call.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".