Background
As the global population continues to age, social realities such as advanced age, disability and living alone are coming to the fore, and the demand for medical care and health services for the elderly is increasing dramatically, especially in geriatrics. Given the important role geriatric nurses play in the diagnosis and treatment of diseases and rehabilitation of elderly patients, and due to the uniqueness and complexity of geriatric work, this requires geriatric nurses not only to have the competencies that are available in general nursing, but also to ensure that they have sufficient geriatric core competencies in order to effectively meet the needs of the patients and accelerate their recovery. Although previous studies have investigated the core competencies of nursing staff, there has been little research on geriatric nurses’ core geriatric nursing competencies and their predictors. The aim of this study was to investigate the current status of the geriatric nursing competency inventory (GNCI) among geriatric nurses using latent profiling, to identify potential subgroups and their population characteristics, and to explore the factors that influence the potential subgroups.
Methods
From January to March 2024, 1,313 geriatric nurses in Hefei City were selected by stratified cluster sampling method and surveyed with general information questionnaire, geriatric nursing competency inventory, and occupational coping self-efficacy scale for nurses(OCSE-N). Potential subgroups of GNCI differences among geriatric nurses were identified by latent profile analysis (LPA). Multiple logistic regression analyses were used to explore the factors influencing the GNCI of geriatric nurses with different latent profiles.
Results
Geriatric nurses’ OCSE-N was positively correlated with GNCI, and the GNCI score was 123.06(41.60), which indicated that geriatric nurses’ GNCI was at an intermediate level. The OCSE-N score was 35.44(7.34), which was at a relatively high level. There was heterogeneity in the GNCI of geriatric nurses, which was classified into three subgroups i.e., Low-competency group, Medium-competency group, High-competency group. The results of multiple logistic regression analyses showed that OCSE-N, title, whether or not they attended geriatric nurse specialist training, and specialist nurse status were predictors of GNCI among geriatric nurses (P < 0.05).
Conclusion
The GNCI categorical characteristics of geriatric nurses are obvious, and nursing managers should adopt targeted interventions according to the characteristics of nurses in different profiles to improve the overall quality of care.
