Influencing factors and network analysis of latent profiles of care demands among disabled older adults in integrated medical and nursing care institutions in Shanghai, China: a cross-sectional study.

OBJECTIVE

To identify the latent profiles and network structure of care demands among disabled older adults in integrated medical and nursing care institutions, and to provide empirical evidence for service provision.

METHODS

Convenience sampling was used to select 443 disabled older adults from 16 integrated medical and nursing care institutions in Pudong New Area, Shanghai. Face-to-face surveys were conducted using a general information questionnaire and a service demands questionnaire. Latent profile analysis was employed to identify care demand profiles and their influencing factors, while network analysis was applied to construct the demand network and compare network differences across profiles.

RESULTS

Latent profile analysis identified three care demand profiles: the "Low-demand expression group" (76.4%), the "Multidimensional high-demand group" (18.5%), and the "Psychological-prominent demand group" (5.1%). Age, gender, pre-retirement occupation, and frequency of children's visits were independent influencing factors for profile membership (P<0.05). The overall network analysis revealed that chronic disease management health education and safety prevention health education served as bridge demands. In both the overall and subgroup network analyses, excretion care and rehabilitation assessment were core demands. Subgroup comparison showed that the strongest edge in the "Low-demand expression group" crossed dimensions, whereas the strongest edge in the "Multidimensional high-demand group" was concentrated within dimensions.

CONCLUSION

The care demands of disabled older adults in integrated medical and nursing care institutions are typologically differentiated and shaped by multiple factors. The association structures among care demands differ fundamentally across demand types. Therefore, institutions should consider both the demand classification and network structures when allocating care resources, rather than determining service priorities based solely on the average scores of individual demand items.