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To examine how gender differences in the nursing work environment shape nurses' perceived quality of care and to identify gender-specific predictors and evaluative mechanisms.
A mixed-methods design was employed, integrating quantitative data analysis with qualitative in-depth individual interviews.
This study was conducted in two phases: The first phase was a quantitative analysis, based on a large national dataset from the 2017 Chinese Nursing Work Environment Survey (N = 16,382), in which secondary analysis was performed using hierarchical linear regression, relative importance analysis, and network analysis to identify key predictors. The second phase was a qualitative study, in which in-depth individual interviews were conducted with 30 clinical nurses (15 male and 15 female), and thematic analysis was applied to explore gender-differentiated experiences.
The core finding of this study is that gender-differentiated factors within the work environment significantly shape nurses' perception of care quality. Quantitative results showed that the strongest predictor for female nurses was professional development, whereas recognition of value was most salient for male nurses. Qualitative results corroborated these findings: female nurses emphasised continuing education and emotional support, while male nurses emphasised fair evaluation and professional identity. Both groups reported that high-intensity workloads hindered the delivery of ideal humanistic care, inducing moral distress and emotional suppression and exposing ethical gaps in organisational support.
Gender differences in the nursing work environment shape pathways to perceived care quality and expose deeper managerial and ethical challenges. A gender-sensitive, ethics-oriented management approach can enhance nurse satisfaction and care quality, providing empirical support for optimising workforce allocation and sustaining healthcare systems.
Findings direct nurse leaders to tailor improvement strategies—enhancing professional-development infrastructure for women and strengthening recognition mechanisms for men—while embedding explicit ethical support to reduce moral distress and improve both workforce well-being and patient outcomes.
No patient or public contribution.
To classify the unmet integrated care needs of older adults with multimorbidity and to explore the factors associated with different categories of unmet integrated care needs among the target population.
A cross-sectional survey using the statistical method of latent profile analysis.
From July 2022 to March 2023, 397 older adults with multimorbidity, aged 60 years or older, were recruited from one primary healthcare setting and from four secondary and tertiary hospitals to participate in face-to-face questionnaire surveys. The questionnaire used in this study to assess unmet integrated care needs among older adults with multimorbidity was self-designed through a series of steps, including a scoping review, expert consultation and cognitive interviews. Latent profile analysis was applied to uncover distinct profiles of unmet integrated care needs, and multinomial logistic regression was employed to explore whether the profiles were further distinguished by participants' sociodemographic and health-related covariates. The data were analysed using IBM SPSS v.29.0 and Mplus v.8.0.
The optimal solution was a four-profile model, characterised by high unmet integration needs, high unmet system integration needs, low unmet system integration needs and low unmet integration needs, respectively. Multinomial logistic regression results indicated that profile differences were associated with place of residence, number of coresidents and the presence or absence of complex multimorbidity.
The integrated care needs of older adults with multimorbidity have not yet been fully met. Classifying and characterising unmet integrated care needs profiles is a crucial step in the rational allocation of integrated care resources.
This study was reported based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) for cross-sectional studies.
All participants were older adults with multimorbidity, and they were informed that they could withdraw from the study at any time.
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