The relationship between nursing leadership and nurses' job satisfaction in Canadian oncology work environments
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".