Internal medicine trainee perspectives on back‐up call systems and relationships to burnout
Bibliographic record
Abstract
INTRODUCTION: As burnout within medicine escalates, residency programmes should strive to understand how training structures may contribute. Back-up call systems that address gaps in overnight resident call coverage are one possible contributing structure. However, the intersection between back-up call policies and burnout remains unclear. The authors explored residents' decision-making process when deciding whether or not to activate a back-up resident for call coverage, perspectives surrounding the legitimacy of call activations and the impact of back-up call systems on education and experienced burnout. METHODS: Internal medicine residents at the University of Toronto were recruited through email. Eighteen semi-structured one-on-one interviews were conducted with residents from September 2019 to February 2020. Interviews explored participants' experiences and perceptions with back-up call and call activations. A constructivist grounded theory approach was used to develop a conceptual understanding of the back-up system as it relates to residents' decisions underlying activations, downstream impacts and relationships to burnout. RESULTS: Residents described a complex thought process when deciding whether to activate back-up. Decisions were coloured by inner conflicts including sense of collegiality, need to maintain an image and time of year balanced against self-reported burnout. Residents described how back-up calls can lead to burnout, usually in the form of exhaustion, lowering their threshold to trigger future back-up activations. Impacts included anxiety of not knowing whether an activation would occur, decreased educational productivity and the 'domino effect' of increased workload for colleagues. DISCUSSION: Residents weigh inner tensions when deciding to activate back-up. Their collective experience suggests that burnout is both a trigger and consequence of back-up calls, creating a cyclical relationship. Escalating rates of call activations may signal that burnout amongst residents is high, warranting educational leads to assess for resident wellness and to critically evaluate the structure of such systems with respect to unintended consequences.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".