Work-Life Balance Interventions for Nurses: A Scoping Review.

BACKGROUND

Intense workloads and irregular scheduling put global nursing work-life balance at risk. While healthcare systems frequently try to implement and evaluate strategies to mitigate this, the existing evidence base is limited by conceptual ambiguity, intervention heterogeneity and a lack of nurse-specific designs.

AIM: To synthesise interventions designed to improve work-life balance and explore their effectiveness in enhancing work-life balance among nursing professionals.

DESIGN

A scoping review.

DATA SOURCE: Scopus, Web of Science, CINAHL, PubMed, EMBASE and PsycINFO. Grey literature was searched on ProQuest.

METHODS

This review adhered to the five-stage framework established by Arksey & O'Malley. Articles published from inception up to October 2024 were searched. Studies were eligible if they involved registered nurses or nursing professionals as the primary population, evaluated or described interventions aimed at improving work-life balance or related constructs (e.g., work-life conflict), reported empirical findings and were published in English. Data were charted with a standardised form created iteratively by the review team. Extracted variables included study characteristics, participant demographics, intervention type and duration, work-life balance outcome measures and key findings. Reporting followed the PRISMA-ScR Checklist guidelines.

RESULTS

This review included twenty-two studies. Findings were categorised into the following four intervention types: (1) wellness programme, (2) training programme, (3) work digitalisation and (4) work flexibility.

CONCLUSION

This paper highlighted the complex nature of work-life balance and the importance of a comprehensive approach to help nurses manage their professional and personal roles.

IMPLICATIONS FOR THE NURSING PROFESSION AND HEALTH CARE PRACTICE: Insights from this review can guide administrators, managers, educators and policymakers in developing work-life balance interventions, particularly through AI-enabled digitalisation of nursing work and greater flexibility in work arrangements, while also informing future research to advance this underdeveloped concept.