Burnout in emergency department healthcare professionals is associated with coping style: a cross-sectional survey
Bibliographic record
Abstract
INTRODUCTION: Ineffective coping may lead to impaired job performance and burnout, with adverse consequences to staff well-being and patient outcomes. We examined the relationship between coping styles and burnout in emergency physicians, nurses and support staff at seven small, medium and large emergency departments (ED) in a Canadian health region (population 500,000). METHODS: Linear regression with the Coping Inventory for Stressful Situations (CISS) and Maslach Burnout Inventory (MBI) was used to evaluate the effect of coping style on levels of burnout in a cross-sectional survey of 616 ED staff members. CISS measures coping style in three categories: task-oriented, emotion-oriented and avoidance-oriented coping; MBI, in use for 30 years, assesses the level of burnout in healthcare workers. RESULTS: Task-oriented coping was associated with decreased risk of burnout, while emotion-oriented coping was associated with increased risk of burnout. DISCUSSION: Specific coping styles are associated with varied risk of burnout in ED staff across several different types of hospitals in a regional network. Coping style intervention may reduce burnout, while leading to improvement in staff well-being and patient outcomes. Further studies should focus on building and sustaining task-oriented coping, along with alternatives to emotion-oriented coping.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".