Latent profile analysis and influence factor study of well-being among nurses in China: a cross-sectional study Article Swipe
Ping-Ping Su
,
Si Chen
,
Feifei Chang
,
Haihuan Feng
,
Xiaoyu Feng
,
Li-yong Wen
,
Hui Hu
,
Maggie Jing Ouyang
,
Xiaobing Wu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1136/bmjopen-2024-095858
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1136/bmjopen-2024-095858
Objective The present study employed latent profile analysis (LPA) to identify three distinct profiles of subjective well-being (SWB) among Chinese nurses. It further examined the factors influencing these profiles and aimed to provide a foundation for targeted interventions to enhance nurses’SWB. Design A cross-sectional study was conducted between November 2023 and March 2024. Setting Data were collected from three Class III Grade A hospitals in China. Participants A total of 2272 nurses were recruited for this study. Outcome measures Data collection used a demographic questionnaire, the SWB Scale, the Nurse Job Satisfaction Scale and the Perceived Social Support Scale. LPA identified distinct SWB characteristics, and influencing factors were analysed using χ 2 tests and multivariable logistic regression analysis. Results Nurses’ SWB was classified into three profiles: (1) high health concern–low well-being (27.3%), (2) moderate health concern–moderate well-being (41.1%) and (3) low health concern–high well-being (31.6%). Multivariable regression analysis revealed significant associations of gender, age, years of experience, professional title, position, self-perceived health, social support and job satisfaction with these profiles (p<0.05). Conclusion Given the heterogeneity of nurses’ SWB identified through LPA, healthcare institutions may design evidence-based interventions tailored to specific profiles (eg, high health concern–low well-being groups) and key predictors (eg, job satisfaction and social support) to promote sustainable well-being and reduce burnout risks.
Related Topics
Concepts
Medicine
Psychological intervention
Scale (ratio)
Cross-sectional study
Social support
Logistic regression
Latent class model
Job satisfaction
Burnout
Life satisfaction
Gerontology
Nursing
Clinical psychology
Psychology
Social psychology
Physics
Statistics
Quantum mechanics
Mathematics
Internal medicine
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjopen-2024-095858
- OA Status
- gold
- Cited By
- 1
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410919432
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410919432Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1136/bmjopen-2024-095858Digital Object Identifier
- Title
-
Latent profile analysis and influence factor study of well-being among nurses in China: a cross-sectional studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-01Full publication date if available
- Authors
-
Ping-Ping Su, Si Chen, Feifei Chang, Haihuan Feng, Xiaoyu Feng, Li-yong Wen, Hui Hu, Maggie Jing Ouyang, Xiaobing WuList of authors in order
- Landing page
-
https://doi.org/10.1136/bmjopen-2024-095858Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1136/bmjopen-2024-095858Direct OA link when available
- Concepts
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Medicine, Psychological intervention, Scale (ratio), Cross-sectional study, Social support, Logistic regression, Latent class model, Job satisfaction, Burnout, Life satisfaction, Gerontology, Nursing, Clinical psychology, Psychology, Social psychology, Physics, Statistics, Quantum mechanics, Mathematics, Internal medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Setting | 53 |
| abstract_inverted_index.Support | 97 |
| abstract_inverted_index.between | 47 |
| abstract_inverted_index.burnout | 212 |
| abstract_inverted_index.enhance | 39 |
| abstract_inverted_index.factors | 25, 106 |
| abstract_inverted_index.further | 22 |
| abstract_inverted_index.gender, | 152 |
| abstract_inverted_index.groups) | 196 |
| abstract_inverted_index.health, | 161 |
| abstract_inverted_index.nurses. | 20 |
| abstract_inverted_index.present | 2 |
| abstract_inverted_index.profile | 6 |
| abstract_inverted_index.promote | 207 |
| abstract_inverted_index.provide | 32 |
| abstract_inverted_index.support | 163 |
| abstract_inverted_index.through | 179 |
| abstract_inverted_index.(27.3%), | 131 |
| abstract_inverted_index.(31.6%). | 144 |
| abstract_inverted_index.November | 48 |
| abstract_inverted_index.analysed | 108 |
| abstract_inverted_index.analysis | 7, 147 |
| abstract_inverted_index.distinct | 12, 101 |
| abstract_inverted_index.employed | 4 |
| abstract_inverted_index.examined | 23 |
| abstract_inverted_index.identify | 10 |
| abstract_inverted_index.logistic | 115 |
| abstract_inverted_index.measures | 78 |
| abstract_inverted_index.moderate | 133 |
| abstract_inverted_index.profiles | 13, 28, 169, 190 |
| abstract_inverted_index.revealed | 148 |
| abstract_inverted_index.specific | 189 |
| abstract_inverted_index.support) | 205 |
| abstract_inverted_index.tailored | 187 |
| abstract_inverted_index.targeted | 36 |
| abstract_inverted_index.Nurses’ | 119 |
| abstract_inverted_index.Objective | 0 |
| abstract_inverted_index.Perceived | 95 |
| abstract_inverted_index.analysis. | 117 |
| abstract_inverted_index.collected | 56 |
| abstract_inverted_index.conducted | 46 |
| abstract_inverted_index.hospitals | 63 |
| abstract_inverted_index.nurses’ | 176 |
| abstract_inverted_index.position, | 159 |
| abstract_inverted_index.profiles: | 125 |
| abstract_inverted_index.recruited | 73 |
| abstract_inverted_index.Conclusion | 171 |
| abstract_inverted_index.classified | 122 |
| abstract_inverted_index.collection | 80 |
| abstract_inverted_index.foundation | 34 |
| abstract_inverted_index.healthcare | 181 |
| abstract_inverted_index.identified | 100, 178 |
| abstract_inverted_index.predictors | 199 |
| abstract_inverted_index.regression | 116, 146 |
| abstract_inverted_index.subjective | 15 |
| abstract_inverted_index.well-being | 16, 130, 136, 143, 195, 209 |
| abstract_inverted_index.demographic | 83 |
| abstract_inverted_index.experience, | 156 |
| abstract_inverted_index.influencing | 26, 105 |
| abstract_inverted_index.significant | 149 |
| abstract_inverted_index.sustainable | 208 |
| abstract_inverted_index.(p<0.05). | 170 |
| abstract_inverted_index.Participants | 66 |
| abstract_inverted_index.Satisfaction | 91 |
| abstract_inverted_index.associations | 150 |
| abstract_inverted_index.institutions | 182 |
| abstract_inverted_index.professional | 157 |
| abstract_inverted_index.satisfaction | 166, 202 |
| abstract_inverted_index.Multivariable | 145 |
| abstract_inverted_index.concern–low | 129, 194 |
| abstract_inverted_index.heterogeneity | 174 |
| abstract_inverted_index.interventions | 37, 186 |
| abstract_inverted_index.multivariable | 114 |
| abstract_inverted_index.nurses’SWB. | 40 |
| abstract_inverted_index.concern–high | 142 |
| abstract_inverted_index.evidence-based | 185 |
| abstract_inverted_index.questionnaire, | 84 |
| abstract_inverted_index.self-perceived | 160 |
| abstract_inverted_index.cross-sectional | 43 |
| abstract_inverted_index.characteristics, | 103 |
| abstract_inverted_index.concern–moderate | 135 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.92947557 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |