Cross‐national comparison of factors related to stressors, burnout and turnover among nurses in developed and developing countries
Bibliographic record
Abstract
AIM: To examine factors of a hypothetical model related to stressors, burnout and turnover in nurses from developed and developing countries-Canada, Japan, the United States, Malaysia and Thailand. DESIGN: A cross-sectional questionnaire-based study. METHODS: Conducted between April 2016 and October 2017, the Maslach Burnout Inventory, Intention to Leave Scale, and Nursing Stress Scale collected data from acute care hospital nurses in Canada (n = 309), Japan (n = 319), Malaysia (n = 242), Thailand (n = 211) and the United States (n = 194). RESULTS: Compared to other countries, burnout "exhaustion" was the highest in Japan and "cynicism" and intention to leave the job were the highest in Malaysia. Thailand had lower burnouts and turnover than other countries and higher professional efficacy than Japan and Malaysia. In all countries, reducing stressors is important for reducing burnout and intention to leave jobs, especially as they relate to "lack of support."
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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".