Comparative analysis of work-related factors associated with burnout and its dimensions among nursing faculty in Canada and the United States
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate and compare burnout and its dimensions-exhaustion, cynicism and professional efficacy-across workplace and socio-demographic characteristics among nursing faculty in Canada and the United States (U.S.). BACKGROUND: Burnout among nursing faculty affects the availability and retention of educators, crucial for producing qualified nurses to meet healthcare demands. Despite its significance, research in this area remains limited. DESIGN: A correlational cross-sectional survey was used. METHODS: An online survey was administered to 640 nursing faculty in Canada and 111 in the U.S. Burnout was measured using the Maslach Burnout Inventory and multivariate linear regression identified predictors of burnout. RESULTS: Overall, 62.4 % of participants reported moderate to high burnout. Canadian faculty were primarily involved in undergraduate and graduate education, whereas U.S. faculty devoted more time to service activities. Predictors of burnout and its dimensions varied by country. In Canada, older faculty (≥60 years) and those with a nursing diploma reported lower burnout, while those with a Doctor of Nursing Practice reported higher levels. In the U.S., burnout was higher among younger faculty (≤39 years), those with more teaching hours and lower among non-tenured faculty. CONCLUSION: Factors influencing burnout differ between Canada and the U.S., reflecting variations in academic environments. Tailored interventions, such as workload balancing and targeted support, are essential for addressing burnout and improving faculty retention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| 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".