Institutional and personal determinants of nursing educators’ job satisfaction and turnover intention: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Nursing educators play a critical role in training future nurses, and high turnover can disrupt the training quality and process. This study identified the institutional and personal factors influencing Canadian nursing educators' job satisfaction and turnover intention. METHODS: This cross-sectional study used an online survey to obtain the levels of job satisfaction, turnover intention, role description, and institutional and personal/demographic characteristics of nursing faculty across Canadian institutions. Data were analysed using descriptive statistics, chi-square, bivariate linear regression, and hierarchical linear regression. RESULTS: A total of 645 participants, with a mean ± SD age of 48.82 ± 10.11 years, returned a completed questionnaire. The average/maximum job satisfaction and turnover intention scores were 12.59/20 ± 3.96 and 6.50/15 ± 3.05, respectively. Higher job satisfaction was significantly associated with lower turnover intention (β=-0.559, p < 0.001). The multivariate analysis showed that having a partner or being married (β = 0.086, p = 0.031), working ≤ 40 h weekly (β=-0.235, p < 0.001), teaching ≤ 4 courses annually (β=-0.115, p = 0.007), and having higher than bachelor's degree qualification (β=-0.091, p = 0.042) predicted high job satisfaction, while high turnover intention was associated with faculty in the Prairie region (β = 0.135, p = 0.006) and working ≥ 41 h weekly (β = 0.151, p = 0.001). CONCLUSION: Having a partner, manageable workload, and advanced qualifications positively influenced job satisfaction, while high turnover intention was associated with high workloads. Institutions may benefit from ensuring proportionate faculty workloads, fostering career advancement, and providing robust support systems that can stabilise the workforce and preserve the quality of nursing education.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".