The relationship between nursing leadership and nurses' job satisfaction in Canadian oncology work environments
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Current Canadian oncology work environments are challenged by the same workforce statistics as other nursing specialties: nurses are among the most overworked, stressed and sick workers, and more than 8% of the nursing workforce is absent each week due to illness. AIM: To develop and estimate a theoretical model of work environment factors affecting oncology nurses' job satisfaction. METHODS: The sample consisted of 515 registered nurses working in oncology settings across Canada. The theoretical model was tested as a structural equation model using LISREL 8.54. RESULTS: The final model fitted the data acceptably (chi(2) = 58.0, d.f. = 44, P = 0.08). Relational leadership and physician/nurse relationships significantly influenced opportunities for staff development, RN staffing adequacy, nurse autonomy, participation in policy decisions, support for innovative ideas and supervisor support in managing conflict, which in turn increased nurses' job satisfaction. CONCLUSIONS: These findings suggest that relational leadership and positive relationships among nurses, managers and physicians play an important role in quality oncology nursing environments and nurses' job satisfaction. IMPLICATIONS FOR NURSING MANAGEMENT: Oncology nursing work environments can be improved by focusing on modifiable factors such as leadership, staff development and staffing resources, leading to better job satisfaction and hopefully retention of nurses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 it