Investigating the work–life experiences of nursing faculty in Canadian academic settings and the factors that influence their retention: protocol for a mixed-method study
Bibliographic record
Abstract
INTRODUCTION: While all research-oriented faculty face the pressures of academia, female faculty in fields including science, engineering, medicine and nursing, are especially susceptible to burnout. Nursing is unique in that it remains a predominantly female-dominated profession, which implies that there is a critical mass of females who are disproportionately affected and/or at higher risk of burnout. To date, little is known about the experiences of nursing faculty especially, new and early career researchers and the factors that influence their retention. This study aims to understand the work-life (the intersection of work with personal life) experiences of nursing faculty in Canadian academic settings and the factors that influence their retention. METHODS AND ANALYSIS: A mixed-method design will be used in this study. For the quantitative study, a sample of approximately 1500 new and early career nursing faculty across Canadian academic institutions will be surveyed. Eligible participants will be invited to complete a web-based structured questionnaire in both French and English language. Data will be evaluated using generalised linear regression model and structural equation modelling. Given the complexities of work-life issues in Canada, qualitative focus group interviews with about 20-25 participants will also be conducted. Emerging themes will be integrated with the survey findings and used to enrich the interpretation of the quantitative data. ETHICS AND DISSEMINATION: This study has received ethical approval from the Hamilton Integrated Research Ethics Board (#1477). Prior to obtaining informed consent, participants will be provided with information about study risks and benefits and strategies undertaken to ensure confidentiality and anonymity. The study findings will be disseminated to academics and non-academic stakeholders through national and international conference presentations and peer-reviewed open-access journals. A user-friendly report will be shared with professional nursing associations such as the Canadian Associations of Schools of Nursing, and through public electronic forums (e.g., Twitter). Evidence from this study will also be shared with stakeholders including senior academic leaders and health practitioners, government, and health service policy-makers, to raise the profile of discourses on the nursing workforce shortages; and women's work-life balance, a public policy issue often overlooked at the national level. Such discussion is especially pertinent in light of the disproportionate impact of COVID-19 on women, and female academics. The findings will be used to inform policy options for improving nursing faculty retention in Canada and globally.
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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 | 0.070 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".