Latent profiles of ethical climate and nurses’ service behavior

Abstract

Background Hospital ethical climate has important implications for clinical nurses’ service behavior; however, the relationships are complicated by the fact that five types of ethical climate (caring, law and code, rules, instrumental, and independence) can be combined differently according to their level and shape differences. Recent developments in person-centered methods (e.g., latent profile analysis (LPA)) have helped to address these complexities. Aim From a person-centered perspective, this study explored the distinct profiles of hospital ethical climate and then examined the relationships of the profiles with clinical nurses’ service behavior (both in-role and extra-role service behavior). Research design A quantitative study was conducted using cluster random sampling. Latent profile analysis and binary coded hexadecimal (BCH) analysis were conducted using Mplus 8.2. Participants and research context A total of 871 clinical nurses in China were surveyed using the Ethical Climate Scale and Nurses’ Service Behavior Questionnaire. Ethical considerations Ethical approval was obtained from the IRB of the First Affiliated Hospital of Jinan University (No. KY-2020-090). Results A four-profile hospital ethical climate model provided the best fit for the data. The four different profiles not only varied in level, but also in shape: high normative and low egoism (45.8%), high ethical climate (19.9%), low ethical climate (3.6%), and moderate ethical climate (30.8%). These profiles differentially predicted clinical nurses’ overall, in-role, and extra-role service behaviors. Conclusions The results reveal new insights into the nature of hospital ethical climate and how different clinical nurses in these profiles can be best managed to accomplish different forms of service behavior.

Latent profiles of nurses’ moral resilience and compassion fatigue

“…31 The selection of the optimal model by comparing fit indices of different classification models ensures the accuracy and objectivity of the profile results. 32 This method can ensure inter-group heterogeneity and intra-group homogeneity to the greatest extent. 32 LPA is able to describe group characteristics, predict individual attributes, and uncover hidden relationships.…”

“…32 This method can ensure inter-group heterogeneity and intra-group homogeneity to the greatest extent. 32 LPA is able to describe group characteristics, predict individual attributes, and uncover hidden relationships. 32 This method can compare moral resilience models by identifying different subpopulations of clinical nurses through LPA.…”

“…32 LPA is able to describe group characteristics, predict individual attributes, and uncover hidden relationships. 32 This method can compare moral resilience models by identifying different subpopulations of clinical nurses through LPA. The LPA creates a distinct population, respecting the difference between quantity and quality (shape) of profile indicators, and analyzing their associations with external, auxiliary, and other variables.…”

“…Behavioral and psychosocial studies have used LPA. Previously, LPA has been used to identify subgroups of ethical climate, 32 work engagement, 35 selfdirected learning ability, 36 and moral sensitivity 37 in nursing studies. No research has been conducted on the latent profile of moral resilience in nurses.…”

A latent profile analysis of psychological resilience associated with work fatigue among Chinese nurses

“…By utilizing a probability model, LPA enables the identification of unique subgroups or subpopulations within a larger population (Zhang et al, 2019). This method can then be used to identify the group to which each nurse belongs and guide appropriate intervention efforts that address the unique needs of each group rather than target variables (Zhang et al, 2020;Zhang et al, 2023). Furthermore, this statistical analysis method is novel in nurse psychological resilience studies and addresses a gap in exploring nurses' resilience subtypes during COVID-19.…”