Prevalence and drivers of nurse and physician distress in cardiovascular and oncology programmes at a Canadian quaternary hospital network during the COVID-19 pandemic: a quality improvement initiative
Bibliographic record
Abstract
OBJECTIVES: To assess the prevalence and drivers of distress, a composite of burnout, decreased meaning in work, severe fatigue, poor work-life integration and quality of life, and suicidal ideation, among nurses and physicians during the COVID-19 pandemic. DESIGN: Cross-sectional design to evaluate distress levels of nurses and physicians during the COVID-19 pandemic between June and August 2021. SETTING: Cardiovascular and oncology care settings at a Canadian quaternary hospital network. PARTICIPANTS: 261 nurses and 167 physicians working in cardiovascular or oncology care. Response rate was 29% (428 of 1480). OUTCOME MEASURES: Survey tool to measure clinician distress using the Well-Being Index (WBI) and additional questions about workplace-related and COVID-19 pandemic-related factors. RESULTS: Among 428 respondents, nurses (82%, 214 of 261) and physicians (62%, 104 of 167) reported high distress on the WBI survey. Higher WBI scores (≥2) in nurses were associated with perceived inadequate staffing (174 (86%) vs 28 (64%), p=0.003), unfair treatment, (105 (52%) vs 11 (25%), p=0.005), and pandemic-related impact at work (162 (80%) vs 22 (50%), p<0.001) and in their personal life (135 (67%) vs 11 (25%), p<0.001), interfering with job performance. Higher WBI scores (≥3) in physicians were associated with perceived inadequate staffing (81 (79%) vs 32 (52%), p=0.001), unfair treatment (44 (43%) vs 13 (21%), p=0.02), professional dissatisfaction (29 (28%) vs 5 (8%), p=0.008), and pandemic-related impact at work (84 (82%) vs 35 (56%), p=0.001) and in their personal life (56 (54%) vs 24 (39%), p=0.014), interfering with job performance. CONCLUSION: High distress was common among nurses and physicians working in cardiovascular and oncology care settings during the pandemic and linked to factors within and beyond the workplace. These results underscore the complex and contextual aspects of clinician distress, and the need to develop targeted approaches to effectively address this problem.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".