Prevalence of burnout and impact of workload on physician wellness: A cross‐sectional survey of hospitalists in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Hospitalists in British Columbia care for a large percentage of hospitalized patients across 21 acute care facilities. OBJECTIVE: We aimed to characterize the demographic and work attributes of the workforce and to understand levels of burnout and the relationship between workload and job satisfaction. METHODS: We conducted a cross-sectional survey of individuals participating in hospitalist programs in BC. RESULTS: Almost all individuals (96%) were involved in the care of patients with COVID-19 in 2021, the height of the pandemic. High rates of burnout were demonstrated among hospitalists, with a large number of providers planning to significantly reduce or stop their involvement in acute care. Regression analysis identified workload as an important factor associated with burnout. Older physicians, those who had been practising for longer, and those with moderate to high number of shifts were more likely to consider reducing their involvement with their programs. CONCLUSIONS: High levels of burnout are associated with a desire to reduce work involvement among BC hospitalists. Health system leaders need to consider factors contributing to burnout as a key aspect of broader health human resource planning efforts.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".