The quality of work life of registered nurses in Canada and the United States: a comprehensive literature review
Bibliographic record
Abstract
BACKGROUND: Workplace environment is related to the physical and psychological well-being, and quality of work life (QWL) for nurses. OBJECTIVE: The aim of this paper was to perform a comprehensive literature review on nurses' quality of work life to identify a comprehensive set of QWL predictors for nurses employed in the United States and Canada. METHODS: Using publications from 2004-2014, contributing factors to American and Canadian nurses' QWL were analyzed. The review was structured using the Work Disability Prevention Framework. Sixty-six articles were selected for analysis. RESULTS: Literature indicated that changes are required within the workplace and across the health care system to improve nurses' QWL. Areas for improvement to nurses' quality of work life included treatment of new nursing graduates, opportunities for continuing education, promotion of positive collegial relationships, stress-reduction programs, and increased financial compensation. CONCLUSIONS: This review's findings support the importance of QWL as an indicator of nurses' broader work-related experiences. A shift in health care systems across Canada and the United States is warranted where health care delivery and services are improved in conjunction with the health of the nurses working in the system.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".