Nurses in psychiatric hospitals are essential in providing care for patients with mental health disorders. However, psychiatric nursing can be emotionally and physically demanding, with nurses frequently facing challenging patient behaviors, high patient acuity, and emotionally charged environments (Sule & Mustapha, 2023). High levels of workload and stress are linked to nurse burnout, a condition characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment (Ogunleye et al., 2023). Burnout not only affects the well-being of nurses but can also impact patient care quality, increasing the likelihood of medical errors and reducing overall healthcare effectiveness (Abdul & Yakubu, 2024).
In Sokoto State, psychiatric hospitals are understaffed, with nurses often managing a large number of patients with varying levels of severity in their conditions. The combination of a high workload, limited resources, and the emotionally taxing nature of psychiatric care makes nurses in these settings particularly vulnerable to burnout (Ahmed & Yusuf, 2023). This study seeks to examine the relationship between nurses' workload and burnout in psychiatric hospitals in Sokoto State and explore strategies to reduce burnout and improve nurse well-being and patient care.
Nurses working in psychiatric hospitals in Sokoto State are at risk of experiencing burnout due to the high workload, challenging patient behaviors, and emotional demands of psychiatric care (Mustapha et al., 2023). Burnout among nurses can lead to decreased quality of patient care, high turnover rates, and increased absenteeism, all of which have negative implications for healthcare delivery in psychiatric settings (Tariq & Haji, 2024). Understanding the impact of workload on nurse burnout is essential for developing strategies to alleviate stress and improve the working conditions of nurses in psychiatric hospitals in Sokoto State.
This study will focus on nurses working in psychiatric hospitals in Sokoto State. Limitations include potential bias in self-reported data regarding workload and burnout levels and the ability to generalize findings to other healthcare settings.
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