Differences in Nursing Workplace Quality Between Rural and Urban Hospitals: A Quantitative Analysis
DOI:
https://doi.org/10.64397/nepj.v01i03.2026.a29Keywords:
Quality, Nursing Workplace, Rural Hospitals, Urban Hospitals, Job satisfaction, workplace environment, Support Programs and Services, Physical Working ConditionsAbstract
Introduction: The quality of nursing workplaces differs across rural and urban settings, influencing both staff performance and patient care. This study quantitatively examined workplace quality in one rural and one urban hospital, focusing on workload, available resources, and nurses’ experiences, with the goal of identifying location-specific strategies to improve working conditions
Aim: This study aims to analyze the quality of nursing workplaces in rural and urban hospitals, with a particular focus on identifying key similarities and differences in workplace conditions.
Methods: A quantitative descriptive design was employed, and data were collected using a modified Nursing Workplace Quality Survey administered through physical and online forms. Descriptive statistics were used to analyze five workplace dimensions: Workplace Environment, Personnel Relationships, Physical Working Conditions, Job Perception, and Support Programs and Services.
Results: Findings revealed strong interpersonal relationships in both hospital settings, but notable gaps remained: Physical Working Conditions scored lowest in the rural hospital, while Support Programs and Services were least adequate in the urban hospital.
Conclusion: These results highlight the importance of strengthening infrastructure in rural areas and expanding support systems in urban settings to promote nurse motivation, job satisfaction, and overall performance. The study recommends strategic initiatives such as improved staffing, facility upgrades, mentorship, wellness programs, and burnout prevention. This may be done by conducting a one-day seminar to the concerned offices to relay the results of the study. Future research should expand to larger samples and utilize more robust measurement tools for greater generalizability and reliability.
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