Key Factors Associated with Nursing Care Intensity for Nursing Service Management in a Psychiatric Hospital: A Cross-Sectional Study Approach - Milkhatun Milkhatun; Yudhi Fajar Saputra; Alfi Ari Fakhrur Rizal; Ni Wayan Wiwin Asthiningsih; Rusni Masnina; Maridi M Dirdjo (Ahmar Metastasis Health Journal, 2026)
Nursing care intensity is an important indicator of nursing service quality and patient safety. However, in many clinical settings, nursing workload and care demand assessments are still largely based on medical diagnoses rather than psychosocial and cognitive conditions that may better reflect patients’ actual nursing needs. This study examined the association between patient-related factors across four domains (health history and genetic factors, emotional and cognitive functioning, self-concept, and social–interpersonal relationships) and nursing care intensity levels. A cross-sectional study was conducted at RSJD Atma Husada Mahakam Samarinda, Indonesia, using data from 491 patients collected through a standardized nursing care intensity assessment instrument based on the NANDA–NIC–NOC framework. Nursing care intensity was categorized into low, moderate, and high levels, with most patients classified in the moderate category. Associations between variables and nursing care intensity were analyzed using Chi-square tests, and the strength of associations was evaluated using Cramer’s V and Phi coefficient to identify key associated factors. The result showed that most psychosocial, emotional, cognitive, and social variables showed significant associations with nursing care intensity. The strongest associationships were observed for self-esteem (V = 0.487), socialization in the community (V = 0.408), emotion (V = 0.405), perception (V = 0.290), and intelligence (V = 0.280). In contrast, genetic factors and medical diagnosis showed no significant association with nursing care intensity. Nursing care intensity is more strongly associated with psychosocial and cognitive factors than by traditional medical indicators. These findings highlight key associated factors identified through bivariate analysis and suggest that integrating these domains into routine nursing assessments may support evidence-based staffing allocation, reduce missed nursing care, and improve nursing service quality.