Job Satisfaction Among Nursing Faculty in Canada and the United States
Bibliographic record
Abstract
Background: Higher education wants a satisfied workforce to ensure the organization reaches their stated or evolving goals; however, if faculty are dissatisfied, there can be harmful and long-term consequences on productivity and organizational outcome. This study examined nursing faculty's job satisfaction and intent to stay in universities in the United States and Canada. Method: This study used a nonexperimental, survey research design with correlational analysis. The sample included 746 U.S. and Canadian nursing faculty. A secondary data source from the Collaborative on Academic Careers in Higher Education also was used; the data contained responses to an online survey. Results: Job satisfaction demonstrated statistically significant positive relationships with personal and family policies, collaboration, tenure clarity, institutional leadership, shared governance, and engagement. Conclusion: Understanding the different factors influencing job satisfaction and intent to stay is one step toward meeting the challenge of a diversified academic nursing workforce. [ J Nurs Educ . 2022;61(11):617–623.]
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".