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Examine the relationships between workplace trust, interpersonal trust, and nurses' physical and mental health, and specifically investigate the mediating role of resilience.
Nurses are central to healthcare delivery but frequently experience workplace violence, adversely affecting their well-being. Trust represents a higher-order mechanism that fosters positive attitudes and professional growth, potentially safeguarding nurses' resilience in coping with adversity. However, research elucidating how trust influences nurses' health via resilience remains limited.
A cross-sectional study was conducted using convenience sampling. A total of 2855 clinical nurses from general hospitals in Fujian Province, China, were surveyed between August and October 2022. Workplace trust and interpersonal trust were served as independent variables, Physical Component Summary and Mental Component Summary scores as dependent variables, and resilience as a mediator. Mediation analysis was performed using Mplus 8.3. The study was prepared and reported according to the STROBE checklist.
Mean scores were Physical Component Summary: 51.12 ± 8.90, and Mental Component Summary: 48.20 ± 10.18. Workplace trust had significant direct effects on both Physical Component Summary and Mental Component Summary. Interpersonal trust had no significant direct effects on Physical Component Summary or Mental Component Summary. Resilience demonstrated significant mediating effects: for workplace trust on Physical Component Summary and on Mental Component Summary; and for interpersonal trust on Physical Component Summary and on Mental Component Summary.
Workplace trust directly enhances nurses' physical and mental health. While interpersonal trust lacks a direct link to health outcomes, both workplace and interpersonal trust significantly improve nurses' health indirectly by bolstering resilience. Resilience serves as a critical pathway through which trust fosters well-being.
No patient or public contribution.
Nurse managers and healthcare administrators should prioritise interventions to cultivate workplace trust (e.g., fostering trust among colleagues, and between nurses and the organisation/management) and strengthen interpersonal trust and psychological resilience. Enhancing these protective factors will better equip nurses to manage occupational and personal stressors, ultimately safeguarding and improving their physical and mental health.
To quantify how specific patient-level characteristics influence the actual amount of mobilisation received during ICU care, thereby identifying key predictors to support individualised mobilisation strategies.
A prospective observational study was conducted in four tertiary hospitals among a convenience sample of 141 critically ill patients from July to November 2023. Data on mobilisation and patient characteristics were collected using standardised data collection tools, including a mobilisation log and a demographic information sheet. Data were analysed using non-parametric tests, Spearman correlation analysis, and multivariate regression to examine associations between early mobilisation and patient-related factors.
Males and surgical patients engaged in more activity (p < 0.001). Muscle strength (r = 0.568, p < 0.001) and haemoglobin levels (r = 0.207, p = 0.014) were positively associated with mobilisation, while higher disease severity (r = −0.321, p < 0.001) and greater pain (r = −0.284, p < 0.001) were linked to reduced activity. Muscle strength, disease severity, surgical status, and sex were independent predictors, explaining 32.5% of the variance.
Early mobilisation in the ICU is influenced by various patient-related factors. Protocols should be tailored to individual patient profiles to enhance outcomes.
This study provides guidance for ICU clinicians to develop targeted mobilisation strategies that consider patients' specific clinical profiles. Tailored approaches may help optimise early mobilisation practices and patient outcomes.
Individuals with systemic lupus erythematosus (SLE) often suffer from sleep disturbance, which exhibits heterogeneity. Whether it could be grouped into different clusters remains unknown, posing challenges to the development of personalised interventions to address sleep disturbance.
To examine clusters of sleep disturbance and associated factors in people with SLE.
Cross-sectional design.
From November 2023 to January 2024, people diagnosed with SLE were recruited by a convenience sampling approach. Data were collected via an online platform Wenjuanxing. Sleep disturbance was evaluated by the Pittsburgh Sleep Quality Index (PSQI). Other information, such as disease activity, pain, fatigue, depression and anxiety was also collected using validated instruments. Latent profile analysis was performed to reveal the distinct clusters of sleep disturbance. Multiple logistic regression analysis was performed to investigate factors associated with the clusters.
A total of 538 participants were included, with a response rate of 85.1% (538/632). Only those with sleep disturbance (PSQI > 5) were included in the final analyses. Participant mean age was 32.9 (SD = 8.4) years and 402 (92.6%) were females. All had sleep disturbance (PSQI > 5) and their mean PSQI was 8.8 (SD = 2.9). Three distinct clusters were identified: mild sleep disturbance with poor sleep quality, adequate sleep duration and good daytime functioning (50.7%), mild sleep disturbance with poor sleep quality, adequate sleep duration and poor daytime functioning (30.9%) and moderate sleep disturbance with poor sleep quality, inadequate sleep duration and impaired daytime functioning (18.4%). There are both overlaps and unique aspects in terms of factors associated with each cluster of sleep disturbance, including age, body mass index, cardiovascular system damage, musculoskeletal system damage, depression and anxiety.
Sleep disturbance in patients with SLE showed three distinct clusters, with each cluster having slightly different predisposing factors.
In clinical practice, nurses are recommended to prioritise assessment and interventions for those at-risk subgroups. They could also use the above information to develop and provide personalised interventions to address the unique needs of each cluster of sleep disturbance.
Checklist for reporting of survey studies.
No patient or public contribution.
We aimed to elucidate the underlying mechanisms influencing Oral nutritional supplementation (ONS) adherence in postoperative patients with gastric cancer (GC) by developing a structural equation model.
ONS represents a cost-effective nutritional intervention for postoperative patients with GC, with its efficacy largely dependent on sustained patient adherence over time. However, the interrelationships among the quality of discharge teaching (QDT), readiness for hospital discharge (RHD), medication beliefs and adherence to ONS remain inadequately understood.
A convenience sample of 505 postoperative patients with GC was recruited from January 1, 2023, to December 1, 2024, for a cross-sectional survey conducted at a tertiary-grade A specialised oncology hospital. The data of this study were subjected to descriptive analysis, Harman's one-way analysis of variance, Pearson correlation analysis and mediation effect analysis.
The STROBE checklist was employed for reporting in the study.
Pearson correlation analyses revealed that all four variables were significantly interrelated. Structural equation modelling showed that medication beliefs had the strongest correlation with ONS adherence (β = 0.589), followed by readiness for hospital discharge (RHD) (β = 0.557) and quality of discharge teaching (QDT) (β = 0.523). The structural equation model demonstrated a robust overall fit.
There was a significant chain mediation effect through RHD and medication beliefs. For the development of targeted intervention strategies to improve ONS adherence, future research should prioritise enhancing QDT, optimising RHD and strengthening patients' medication beliefs.
To help nurses and nursing managers formulate intervention measures to improve QDT, RHD, medication beliefs and ONS adherence in postoperative patients with GC.
FreshRSS 