Datamining techniques - decision tree: new view on nurses' intention to leave

New nurses' turnover intention and clinical belonging, based on latent class analysis (LCA)

AIM

To identify potential categories of clinical belonging among new nurses and explore the relationship between different categories and turnover intention.

DESIGN

A cross sectional study.

METHOD

A cross sectional study was conducted among 348 new nurses from tertiary hospitals in Hainan and Guangdong provinces. A general data questionnaire, clinical belonging scale and turnover intention scale were used for examination. Further, the potential categories were used to analyse the categories of clinical belonging, and latent class analysis was utilized to analyse the relationship between different categories of clinical belonging and turnover intention.

RESULTS

The clinical sense of belonging of new nurses were divided into three groups namely C1, C2 and C3. The C1: poor clinical sense of belonging (8.7%), C2: moderate clinical sense of belonging (57.9%) and C3: rich clinical sense of belonging (33.4%). The risk of the turnover intention of new nurses with 'poor clinical sense of belonging' was 0.62 times that of new nurses with 'rich clinical sense of belonging' (OR = 0.62, p < 0.01), which was 0.24 times that of 'moderate clinical sense of belonging' (OR = 0.24, p < 0.01), the risk of the turnover intention of new nurses with 'moderate clinical sense of belonging' was 0.13 times that of new nurses with 'rich clinical sense of belonging'(OR = 0. 13, p < 0.01).

CONCLUSIONS

The results of our study revealed that in order to enhance the new nurses' sense of belonging, support was most crucial when they were first encountering difficulties. To reduce turnover intention, more structured learning opportunities are also required to maximize learning for newly graduated nurses with various nursing degrees.

PATIENT OR PUBLIC CONTRIBUTION

There are no patient or public contributions in this study.

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