Stemming the Tide: Tackling Retention and Attrition Challenges in Rural and Northern Healthcare to Sustain Canada's Nursing Workforce
Bibliographic record
Abstract
AIM: This study was an investigation of the key factors influencing nurse retention and attrition focusing on the perspectives of current and former nurses within the context of the ongoing nursing shortage exacerbated by the COVID-19 pandemic. DESIGN: This descriptive, cross-sectional study was designed to explore the complex dynamics of nurse retention and attrition in a rural and northern academic hospital in northwestern Ontario. METHODS: An online survey was administered to current and former nurses to compare the perspectives of those with no intention of leaving the organisation, those contemplating departure within the next year, and those who had reduced their work hours in the past 5 years. RESULTS: Of the 288 respondents, 47% indicated no intention to leave and 17% reported having already left the organisation. The primary reasons for attrition included excessive workload demands, challenges maintaining a healthy work-life balance and dissatisfaction with management practices and organisational support. Respondents recommended improving leadership effectiveness, increasing staffing levels and implementing retention-focused initiatives to enhance job satisfaction and reduce turnover. CONCLUSION: This study underscored the urgent need for strategic interventions tailored to retain nursing staff, particularly in rural and northern communities already facing significant recruitment and retention challenges. By addressing workload pressures, enhancing work-life balance, strengthening leadership and offering retention initiatives, health care organisations can improve job satisfaction and reduce attrition. System-level changes are essential to creating a sustainable and supportive environment for nursing professionals. IMPACT: The findings highlight the critical need for immediate action to address the nursing crisis in rural and northern health care settings. They emphasise the importance of systemic interventions aimed at improving staffing levels, leadership practices and overall work conditions to safeguard the future of nursing in these underserved regions. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: This research will contribute to the extant literature on the retention and attrition levels of nursing by offering a unique perspective from a rural and northern academ. The findings may help to guide hospital administrators to develop targeted strategies to enhance nurse retention rates within their organisations. By prioritising nurse satisfaction, these efforts will foster positive nurse-patient interactions and improve overall care outcomes. REPORTING METHOD: This study is reported according to STROBE guidelines.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".