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: We conducted a survey of allied health care staff, including physical, respiratory and occupational therapists, pharmacists, social workers, dietitians and speech-language pathologists, in a cardiovascular centre at 2 quaternary referral hospitals in Toronto, Ontario, between Nov. 27, 2018, and Jan. 31, 2019. The survey tool included the Well-Being Index (WBI), which measures fatigue, depression, burnout, anxiety or stress, quality of life, work–life integration, meaning in work and overall distress; a score of 2 or higher indicated high distress. We carried out standard univariate statistical comparisons using the χ2, 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: The response rate to the survey was 86% (45/52). Thirty-three respondents (73%) reported experiencing burnout in the previous month, and 31 (69%) reported emotional problems. Compared to respondents who perceived fair treatment in the workplace, those who perceived unfair treatment (20 [44%]) were more likely to report emotional problems (17 [85%] v. 13 [54%], p = 0.05), to worry that work was hardening them emotionally (15 [75%] v. 8 [33%], p = 0.008), and to feel down, depressed or hopeless (12 [60%] v. 4 [17%], p = 0.005). Twenty-five respondents (56%) and 13 respondents (29%) reported WBI scores consistent with high (≥ 2) or severe (≥ 5) distress, respectively. Respondents were more likely to have a high WBI score if they perceived unfair treatment or inadequate staffing levels. Our respondents had a higher prevalence of burnout (73.3% v. 53.6%, p = 0.008) and a higher average WBI score (2.6 [SD 2.8] v. 1.7 [SD 2.6], p = 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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".