Job stress and turnover among registered nurses in acute care : a regression analysis
Bibliographic record
Abstract
Nurse turnover needs further exploration in the Canadian experience. This study’s\npurpose was to examine whether job stress, as indicated by burnout and psychological distress,\nexplains turnover among acute care registered nurses. The research questions were: To what\nextent does job stress explain nurses’ intent or likelihood of leaving their position or the nursing\nprofession? What other factors, over and above job stress, explain nurses’ intent or likelihood of\nleaving their position and the nursing profession? This secondary analysis of cross-sectional\nsurvey data from 522 acute care registered nurses in British Columbia was analyzed using\nordinal logistic regression. Burnout, specifically emotional exhaustion, was consistently\npredictive of both intent and likelihood to leave the profession and the position. Emotionally\nexhausted nurses are two times more likely to have intent to leave the profession and 1.5 times\nmore likely to do so. The other factors played a minimal role in explaining turnover.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".