How Many Hours Do Internal Medicine Residents At University Of Toronto Spend Onboarding At Hospitals Each Year? A Cross-sectional Survey Study
Bibliographic record
Abstract
ABSTRACT Background Burnout among medical residents is common. One source of burnout is the need to complete redundant administrative tasks such as onboarding processes at various hospitals. Objectives To quantify the time residents at the University of Toronto spend onboarding at teaching hospitals, to identify areas of redundancy in onboarding processes, and to identify trainee perceptions of onboarding processes. Methods We conducted a quality improvement survey of core internal medicine residents at the University of Toronto where residents rotate through multiple different teaching hospitals. The primary outcome was time spent onboarding. Secondary outcomes included perceptions of the onboarding process, and impact on well-being. Results 41% (N=93) of all Internal Medicine residents completed the survey. Most (n=81, 87%) rotated through at least four hospitals and 24 (26%) rotated through more than 5 in the preceding year. The median number of hours spent on the onboarding process was 5 hours per hospital (IQR 1-8) and these tasks were often completed when trainees were post-call (82%, n=76) or outside of work hours (97%, n= 90). The cumulative number of hours spent each year on onboarding tasks by the 93 trainees was 2325 hours (97 days) which extrapolates to 5625 hours (234 days) for all 225 trainees in the core internal medicine program. Most residents reported high levels of redundancy across hospital sites (n=79, 85%) and felt that their well-being was negatively affected (73%, n=68). Conclusions The median internal medicine resident at the University of Toronto spent 5 hours onboarding for each hospital. There is considerable redundancy and the process contributes to self-reported burnout.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".