On‐call absences and academic recognition: A retrospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: Resident call schedules require careful planning and are vulnerable to unanticipated absences from unpredictable factors. We tested whether unplanned absences from resident call schedules were associated with the likelihood of subsequent academic recognition. METHODS: We examined unplanned absences from call shifts for internal medicine residents at the University of Toronto from 2014 to 2022 (8 years). We identified institutional awards granted at the end of the academic year as an indicator of academic recognition. We defined the resident-year as the unit-of-analysis that started in July and ended in June of the subsequent year. Secondary analyses examined the association between unplanned absences and the likelihood of academic recognition in later years. RESULTS: We identified 1668 resident-years of training in internal medicine. In total, 579 (35%) had an unplanned absence, and the remaining 1089 (65%) had no unplanned absence. Baseline characteristics were similar between the two groups of residents. In total, 301 awards were received for academic recognition. The likelihood of receiving an award at the end of the year was 31% lower for residents who had any unplanned absence compared with those who had no absence (adjusted odds ratio = 0.69, 95% confidence interval 0.51-0.93, p = 0.015). The likelihood of receiving an award was further decreased for residents with multiple unplanned absences compared with those with none (odds ratio 0.54, 95% confidence interval 0.33-0.83, p = 0.008). An absence during the first year of residency was not significantly associated with the likelihood of academic recognition in later years of training (odds ratio 0.62, 95% confidence interval 0.36-1.04, p = 0.081). CONCLUSIONS: The results of this analysis suggest unplanned absences from scheduled call shifts may be associated with a decreased likelihood of academic recognition for internal medicine residents. This association could reflect countless confounders or the prevailing culture of medicine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".