Stemming the Tide: Tackling Retention and Attrition Challenges in Rural and Northern Healthcare to Sustain Canada's Nursing Workforce
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».