Burnout and distress among allied health care professionals in a cardiovascular centre of a quaternary hospital network: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Burnout and distress negatively affect the well-being of health care professionals and the treatment they provide. Our aim was to measure the prevalence of burnout and distress among allied health care staff at a cardiovascular centre of a quaternary hospital network in Canada, and compare outcomes to those for nonphysician employees in the United States. METHODS: , Fisher exact or Kruskal-Wallis test as appropriate to perform univariate comparisons in the sample of respondents. We assessed the relation between a WBI score of 2 or higher and demographic characteristics. We compared univariate associations among WBI data for nonphysician employees in the US who completed the WBI to responses from our participants. RESULTS: = 0.05) than 9096 nonphysician employees in the US. INTERPRETATION: The prevalence of burnout, emotional problems and distress was high among allied health care staff. Fair treatment in the workplace and adequate staffing may lower distress levels and improve the work experience of these health care professionals.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".