Burnout and distress among nurses in a cardiovascular centre of a quaternary hospital network: a cross-sectional survey
Bibliographic record
Abstract
<h3>Background:</h3> Burnout and distress have a negative impact on nurses and the treatment they provide. Our aim was to measure the prevalence of burnout and distress among nurses in a cardiovascular centre at 2 quaternary referral hospitals in Canada, and compare these outcomes to those for nurses at academic health science centres (AHSCs) in the United States. <h3>Methods:</h3> We conducted a survey of nurses practising 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, mental and physical quality of life, work–life integration, meaning in work and distress; a score of 2 or higher on the WBI indicated high distress. We also evaluated nurses’ perception of the adequacy of staffing levels and of fair treatment in the workplace, and satisfaction with the electronic health record. We carried out standard univariate statistical comparisons using the χ<sup>2</sup>, 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 nurses at AHSCs in the US who completed the WBI to responses from our participants. <h3>Results:</h3> The response rate to the survey was 49.1% (242/493). Of the 242 respondents, 188 (77.7%) reported burnout in the previous month; 189 (78.1%) had a WBI score of 2 or higher, and 132 (54.5%) had a score of 4 or higher (indicative of severe distress). Ordinal multivariable analysis showed that lower WBI scores were associated with satisfaction with staffing levels (odds ratio [OR] 0.33, 95% confidence interval [CI] 0.16–0.69) and the perception of fair treatment in the workplace (OR 0.41, 95% CI 0.23–0.74). Higher proportions of our respondents than nurses at AHSCs in the US reported burnout (77.7% v. 60.5%, <i>p</i> < 0.001) and had a WBI score of 2 or higher (78.1% v. 57.0%) or 4 or higher (54.5% v. 32.0%) (both <i>p</i> < 0.001). <h3>Interpretation:</h3> Although levels of burnout and distress were high among nurses, their perceptions of adequate staffing and fair treatment were associated with lower distress. Addressing inadequate staffing and unfair treatment may decrease burnout and other dimensions of distress among nurses, and improve their work experience and patient outcomes.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".