Nursing Practice Today

Factors related to depression among transgender women: A systematic review

Patcharin Krongtham, Ratsiri Thato, Penpaktr Uthis — Sun, 16 Mar 2025 06:50:16 +0330

Background & Aim: Transgender women represent a vulnerable population with a high rate of depression. This systematic review aims to identify and analyze the factors associated with depression in this population.
Methods & Materials: The research protocol was registered with PROSPERO. A systematic search was conducted using PubMed, MEDLINE, Scopus, and ProQuest for studies published between 2014 and 2024. Relevant studies focusing on depression and related factors in transgender women were selected. Data extraction focused on identified factors associated with depression. The reporting of this review adhered to PRISMA guidelines, and the quality of included studies was appraised using JBI’s critical appraisal tools. A narrative synthesis was conducted to synthesize the findings.
Results: From 2,511 records identified in the database, 14 cross-sectional studies were included in the review. The analysis revealed three primary categories of factors related to depression: demographic, psychological, and sociological factors. Key demographic factors included age and insufficient income, both of which were at increased risk of depression. Psychological factors such as self-stigma and self-esteem were associated with higher depression rates. Sociological factors, including family support, peer support, and violence, were also significant predictors of depression in transgender women.
Conclusion: Depression in transgender women is influenced by a complex interaction of demographic, psychological, and sociological factors. These findings underscore the need for tailored nursing interventions that incorporate mental health support.

AI-driven fall risk prediction in inpatients: Development, validation, and comparative evaluation

Chia-Lun Lo, Chia-En Liu, Hsiao Yun Chang, Chiu-Hsiang Wu — Sat, 15 Mar 2025 07:05:50 +0330

Background & Aim: Falls among hospitalized patients pose severe consequences, necessitating accurate risk prediction. Traditional assessment tools rely on cross-sectional data and lack dynamic analysis, limiting clinical applicability. This study developed an AI-based fall risk prediction model using supervised learning techniques to enhance predictive accuracy and clinical integration.
Methods & Materials: This study was conducted at a medical center in Taiwan, excluding pediatric patients due to non-disease-related fall factors. Fall cases were obtained from hospital records, and non-fall cases were stratified based on age and gender to create a balanced 1:1 dataset.
A total of 52 predictive variables were identified and refined to 39 through expert review. The AI model was compared with MORSE, STRATIFY, and HII-FRM using supervised learning with 10-fold cross-validation. Performance was evaluated based on accuracy, sensitivity, and specificity.
Results: The results demonstrated that the AI-based model significantly outperformed traditional fall risk assessment tools in accuracy, sensitivity, and specificity. More importantly, the model’s superior predictive power allows for real-time risk assessment and seamless integration into clinical decision support systems. This integration can enable timely interventions, optimize patient safety protocols, and ultimately reduce fall-related incidents in hospitalized settings.
Conclusion: By automating risk assessment, the AI model can alleviate the workload of healthcare professionals, reducing the time required for manual evaluations and minimizing subjective biases in clinical decision-making. This not only enhances operational efficiency but also allows nursing staff to allocate more time to direct patient care. These findings underscore the transformative potential of AI-driven approaches in healthcare, improving patient safety through data-driven.

Psychometric evaluation of the Farsi version of the stressor scale for emergency nurses

Fazel Dehvan — Mon, 03 Mar 2025 00:00:00 +0330

Background: Emergency department nurses experience unique stressors that contribute to elevated levels of occupational stress. Most existing instruments assess general occupational stress without addressing workplace-specific factors. This study aims to evaluate the psychometric properties of the Farsi version of the Stressor Scale for Emergency Nurses (F-SSEN).

Methods: Face and content validity were assessed by five clinical nurses and five nursing experts, respectively. Construct validity, known group validity, and convergent validity were tested on 198 emergency nurses. The test-retest reliability was evaluated in 21 nurses over a two-week interval. Internal consistency was measured using Cronbach's alpha and McDonald's omega coefficients.

Results: Face and content validity were found to be satisfactory. Exploratory factor analysis (EFA) identified four factors—conflicts, life and death situations, patients' families' actions and reactions, and technical and formal supports—which explained 60.64% of the total variance. Convergent validity showed a correlation of 0.554 between job stress scores based on SSEN and Brief Nursing Stress Scale (BNSS). Known group validity revealed that occupational stress scores were higher in women than in men, and there was a significant negative correlation between occupational stress scores and work experience in the emergency department. Cronbach's alpha and McDonald's omega coefficients were 0.953 and 0.954, respectively, and the intraclass correlation coefficient was 0.943.

Conclusions: The Farsi version of the stressor scale for emergency nurses demonstrates strong psychometric properties, making it reliable for measuring occupational stress in emergency nurses.

Prioritizing just culture: A call to action for patient safety

Esmaeil Moshiri, Ali Abbaszadeh, Seyed Hossein Shahcheragh — Mon, 03 Mar 2025 00:00:00 +0330

Patient safety incidents have emerged as a significant global concern, impacting healthcare systems worldwide, as highlighted by the WHO's report of 134 million adverse events annually in hospitals. Healthcare professionals often hesitate to report such incidents due to stigma and the fear of criticism. To combat this, the implementation of a "no-blame culture," introduced by James Reason in 1997, has gained traction, evolving into the concept of a just culture—an environment fostering open discussion of safety-related information without fear of retribution. This approach facilitates an effective incident reporting system and enhances staff capabilities while building organizational trust and accountability. Previous research indicates that adopting a just culture can lead to increased reporting of patient safety incidents, enabling healthcare staff to learn from them, thus reducing the likelihood of future incidents. It’s critical to recognize that a just culture focuses on systemic issues rather than individual blame, which encourages reporting and corrective action. However, this culture is not yet widespread due to misunderstandings. Healthcare leaders and policymakers are urged to promote a just culture by implementing strategies like transparent reporting mechanisms, clear error identification processes, exemplary leadership, and ongoing assessments, ultimately prioritizing patient safety.

Comparison effect of lavender oil inhalation and tea on sleep quality, fatigue, and pain in hemodialysis patients: A randomized clinical trial

Mon, 17 Feb 2025 09:50:16 +0330

Background & Aim: The pain of needle insertion in arteriovenous fistula, fatigue, and sleep disturbances are common problems in hemodialysis patients. Using lavender products can help reduce these problems. This study aimed to compare the effects of lavender aromatherapy and lavender tea consumption on reducing pain and fatigue, as well as improving sleep quality in hemodialysis patients.
Methods & Materials: This study is an open-label trial conducted at the Dialysis Center at Tabriz University of Medical Science from May to December 2022. Random allocation was done by randomizing the moved blocks. hemodialysis patients were allocated to one of the three study groups: control (n=30), lavender inhalation (n=30), and lavender tea (n=30). At the baseline and the end of the study, Participants' sleep quality with PSQI, fatigue with FSS, and pain of needle insertion in arteriovenous fistula with Vas scale were assessed. The data analysis was performed using SPSS software The Analysis of covariance (ANCOVA) test was used to compare the mean of variables between the study groups.
Results: A comparison of the follow-up scores between 3 groups shows that participants in the lavender tea group and lavender aromatherapy group had a lower score of total score of PSQI (P<0.001), fatigue (P<0.001), and pain(P<0.001) following the intervention compared to the control group. Also, there was no statistically significant difference between the lavender tea group and the lavender aromatherapy group in terms of sleep quality scores (P=0.428), fatigue (P=0.570), and pain (P=0.997).
Conclusion: The findings of this study showed that lavender can be useful in improving the problems of dialysis patients, such as sleep quality, fatigue, and pain caused by needle insertion.