Background This study aimed to identify the types of quality of life (QoL) based on the five dimensions of the EQ-5D and predict factors affecting QoL. Methods A multistage stratified cluster sampling survey was conducted among the staff of 12 general hospitals, 1965 nurses completed the survey, and the data were analysed using SPSS 26.0 and Mplus 8.3 for latent analysis. Results Three latent classes of QoL were identified: low-level (2.8%), pain and discomfort (7.6%), medium-level (47.1%), and high-level (42.5%). The types and characteristics of QoL differed among these latent classes. The low-level group had the lowest EQ visual analogue scale (EQ-VAS) score (F = 75.217, P < 0.001) and the highest K10 score (F = 61.90, P < 0.001). Moreover, increased age (OR = 0.819, 95% CI: 0.817–0.973), never having drunk alcohol (OR = 0.107, 95% CI: 0.023, 0.488), and increased EQ-VAS scores (OR = 0.935, 95% CI: 0.919, 0.952) were protective factors for quality of life, while working in obstetrics and gynaecology (OR = 6.457, 95% CI:1.852, 22.512) and higher K10 scores (OR = 1.153, 95% CI: 1.100, 1.209) were risk factors for quality of life. Conclusion The results indicated significant heterogeneity in the types of QoL and identified predictors of QoL. These findings provide basic information for the development of nursing interventions to improve quality of life and identified specific characteristics that should be considered during intervention development.

Background: This study explored how coping styles mediate the relationship between sleep status and quality of life among night shift nurses. Methods: A multistage stratified cluster sampling survey was conducted among staff at a general hospital from November 2019 to January 2020. A total of 1,170 night shift nurses completed the Pittsburgh Sleep Quality Index (PSQI), EuroQol five-dimensions (EQ-5D), and Simplified Coping Style Questionnaire (SCSQ). Respectively, these measures were used to assess sleep quality, the quality of life, and coping styles. Results: The total sleep status was positively correlated with the quality of life (r = 0.31, P < 0.05) and negative coping (r = 0.18, P < 0.05), negatively correlated with positive coping (r = -0.05, P < 0.05). Negative coping was positively correlated with the quality of life (r = 0.42, P < 0.05). According to the path analysis, sleep status had a positive effect on negative coping (β = 0.18, P < 0.05) and quality of life (β = 0.11, P < 0.05), negative effect on positive coping (β = -0.05, P < 0.05); positive coping had a negative effect on quality of life (β = -0.03, P < 0.05), while negative coping had a positive effect on quality of life (β = 0.02, P < 0.05). Conclusions: Coping style partially mediated the relationship between sleep status and quality of life. This suggests that interventions targeting coping styles may benefit night shift nurses, as such an approach can ultimately enhance the quality of life by improving sleep.