Time‐Based Care Workload and Influencing Factors Among Disabled Inpatients: A Latent Profile Analysis

Objective/Aims To identify distinct Time‐Based Care Workload (TCW) subgroups and examine key influencing factors for higher TCW group among disabled elderly inpatients using Latent Profile Analysis (LPA). Design An exploratory, cross‐sectional LPA design was employed. Background Care workload is important to nursing facilities. It determines caregivers’ well‐being and care quality for disabled inpatients. Nevertheless, its influencing factors are barely studied. Methods A total of 1157 disabled inpatients aged 65 and above and their caregivers were recruited from 5 elderly care institutions in China. The participants were evaluated using the Longshi Scale, standardized care time assessments, and a general questionnaire covering patient characteristics, disease status, dysfunction, and so on. The study utilized LPA to identify classes and characteristics of TCW. A Chi‐square test, Kruskal–Wallis H test, Bonferroni correction, and binary logistic regression (based on the dichotomized high‐workload variable) were used to identify the influencing factors. The predictive ability of predictors for the high‐TCW group was verified through ROC curve analysis. Results The 3‐class latent profile model (low‐, moderate‐, and high‐workload groups) were identified. The results indicated that tracheostomy (OR = 3.922, p < 0.05), nasogastric tube (OR = 2.604, p < 0.05), and dysfunctions, whether single (OR = 3.892, p < 0.05) or multiple (two or more) (OR = 8.286, p < 0.05), were identified as significant independent predictor for the high‐TCW group. The ROC curve based on these three influencing factors has a good ability to distinguish the high‐TCW group (AUC = 0.822, 95% CI: 0.792–0.852). Conclusions This study identified tracheostomy, nasogastric tube, and multiple dysfunctions as independent influencing factors of high TCW among disabled inpatients. The results of ROC curve analysis especially for joint indicators (AUC > 0.8) demonstrated that these predictive factors may help care managers optimize the allocation of care time and provide tailored care time support for patients with potential predicting factors.