Sleep problems are an escalating global health concern, with prevalence estimates ranging from 8.3% to 45%. Physicians are disproportionately affected, with rates around 44% compared with 36% in the general population. In Bangladesh, reported rates range from 32% to 58%, with physicians being particularly vulnerable. Poor sleep among physicians is strongly linked to burnout, medical errors and increased mental health risks. Despite these serious implications, existing evidence from Bangladesh remains fragmented and inconsistent, limiting its utility for health policy and workforce interventions. This review therefore seeks to generate reliable pooled prevalence estimates and identify key determinants of sleep problems among Bangladeshi physicians.
The research team will search the PubMed, Scopus, Web of Science, EMBASE, PsycInfo, ProQuest Medical, CINAHL, Google Scholar and BanglaJOL electronic and regional databases following Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines for published studies from inception until 1 August 2025, using truncated and phrase-searched keywords, relevant medical subject headings and citation chaining from grey literature. Observational cross-sectional studies published within the predefined timeframe, using validated assessment tools, and published in English or other major international languages will be prioritised for inclusion. Review papers, case reports, case series, intervention studies, commentaries, preprints, meeting abstracts, protocols, unpublished articles and letters will be excluded. Two independent reviewers will screen the retrieved papers using the Rayyan web-based application, with any disagreements resolved by a third reviewer. Quantitative estimates of sleep problems, including prevalence, duration, quality and associated risk factors among Bangladeshi physicians will be extracted. A narrative synthesis and meta-analysis will be performed to assess the pooled prevalence using a random effects meta-analysis model. Forest and funnel plots will be generated for visualisation. Heterogeneity will be assessed using the I2 statistic, with sensitivity or subgroup analysis conducted as required. The quality of included studies will be evaluated using Joanna Briggs Institute critical appraisal tools for observational study designs. All statistical analysis will be conducted using Jamovi V2.7.6, R V4.3.2 ‘meta’ packages and GraphPad Prism V9.0.2.
This review will synthesise evidence from existing published literature. While completing the findings, the findings will be submitted to an international peer-reviewed journal and disseminated through conferences, policy forums and stakeholders’ networks to inform future research and interventions.
CRD420251123294.
Economic evaluations are essential for informing healthcare resource allocation. When conducted from a societal perspective, they may include productivity costs such as paid and unpaid productivity losses for patients and their caregivers. Although several methods exist to measure and value productivity costs, there is limited methodological consensus on which methods should be used. This scoping review aims to synthesise existing methods for measuring and valuing patient and caregiver productivity costs.
This review will follow the Arksey and O’Malley framework, enhanced by subsequent methodological guidance from Levac and the Joanna Briggs Institute. The six stages include identifying the research question; identifying relevant studies; selecting studies; charting the data; collating, summarising and reporting the results; and consultation. We will search MEDLINE, Embase and EconLit from 1996 to July 2025. Eligible sources will include peer-reviewed literature that reports methods for the measurement or valuation of productivity costs related to paid or unpaid work among patients or caregivers. Screening and data extraction will be conducted independently by two reviewers. Extracted data will include types of productivity costs, instruments used, valuation approaches, as well as recommendations on preferred measurement and valuation methods. Results will be synthesised thematically and reported using the Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews checklist.
Ethics approval is not required as this review will rely exclusively on publicly available literature and does not involve human participants or the use of primary data. The findings will first be shared with Canada’s Drug Agency as a report and then disseminated through peer-reviewed publication and academic presentations to inform future research and practice.
This protocol has been registered with the Open Science Framework (https://doi.org/10.17605/OSF.IO/FK9D4).
Neonatal haemochromatosis, considered to be a gestational alloimmune liver disease (NH-GALD), is a rare but serious disease that results in fulminant hepatic failure. The recurrence rate of NH-GALD in a subsequent infant of a mother with an affected infant is 70%–90%. Recently, antenatal maternal high-dose intravenous immunoglobulin (IVIG) therapy has been reported as being effective for preventing recurrence of NH-GALD in a subsequent infant. However, no clinical trial has been conducted to date.
This is a multicentre open-label, single-arm study of antenatal maternal high-dose IVIG therapy in pregnant women with a history of documented NH in a previous offspring. The objective of this study is to evaluate the efficacy and safety of antenatal maternal high-dose IVIG therapy in preventing or reducing the severity of alloimmune injury to the fetal liver.
The clinical trial is being performed in accordance with the Declaration of Helsinki. The trial protocol was approved by the Clinical Research Review Board at four hospitals. Before enrolment, written informed consent would be obtained from eligible pregnant women. The results are expected to be published in a scientific journal.
28 October 2024, V.8.0.
jRCT1091220353.
by Pornkamol Tiranaprakij, Sahaphume Srisuma, Krongtong Putthipokin, Sirasa Ruangritchankul
BackgroundAnticholinergic medication use is associated with adverse clinical outcomes, especially in older adults. However, few studies have assessed the anticholinergic burden in the Thai geriatric population. Hence, we aimed to evaluate the impact of anticholinergic burden on clinical outcomes in older patients after discharge from the hospital.
MethodsA prospective cohort study was conducted between January 1 to December 31, 2023. The prescribed medications were assessed at admission and discharge to determine the anticholinergic cognitive burden (ACB) scores. Participants were classified into three groups according to the ACB score at discharge: none (score 0), moderate (score 1–2), and severe (score ≥ 3) anticholinergic burden. The Cox proportional hazard model was used to determine the marker risk of high anticholinergic burden to adverse outcomes.
ResultsThis study involved 290 older patients admitted to general internal medicine wards. At discharge, 37.9% (n = 110) of the patients had a high anticholinergic burden (ACB score ≥ 3), and 50% (n = 145) had a higher ACB score than at admission. The three most commonly prescribed anticholinergics at discharge were benzodiazepines (20.3%), corticosteroids (20.0%), and antihistamines (15.9%). During the one-year follow-up period, 16.6% (n = 48) of the patients died. The incidence rate of all-cause mortality in hospitalized older patients with an ACB score ≥ 3 was 0.65 cases per 1000-person day during a one-year follow-up period. After adjusting for potential factors, an ACB score of ≥ 3 at discharge was marginally associated with one-year mortality post discharge [hazard ratio: 2.98, 95% confidence interval (0.96–9.28)].
ConclusionsThe exposure to high anticholinergic burden (ACB scores ≥ 3) at discharge was slightly associated with an increased risk of one-year mortality post discharge. The cautious use of benzodiazepines may assist to reduce the anticholinergic burden in this vulnerable population.
Alzheimer’s disease (AD) impacts over 55 million individuals worldwide and remains the leading cause of dementia (60–70% of cases). By 2050, South and Southeast Asia are projected to have an older adult population more than double, bearing a major share of Alzheimer’s disease burden. This will exert a heavy strain on healthcare systems, particularly in resource-limited countries where support and infrastructure are already stretched. Despite this, no review has yet explored the regional epidemiology and associated risk factors in this context. Thus, this study protocol outlines to synthesise prevailing evidence from these densely populated regions, particularly low- and middle-income nations within South and Southeast Asia.
This review will include studies that reported epidemiological characteristics including prevalence, age of onset, mortality, and risk factors of AD and related dementias comprising in South and Southeast Asian regions. Studies published in any language from inception to date will be extracted from PubMed, Scopus, CINAHL, EMBASE and APA PsycNet, following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines. We will also search grey literature sources and screen the reference lists of the articles selected for full-text review to identify additional relevant studies. Observational studies including case–control, cohort, and cross-sectional designs reporting desired outcomes will be included and appraised for quality assessment with the modified Newcastle-Ottawa Scale (mNOS). The included articles will be appraised by two independent reviewers, with a third resolving any conflicts. Pooled estimates of prevalence, age of onset and mortality will be analysed using random effect meta-analysis (REML) model. Associated risk factors, including modifiable and non-modifiable will be narratively synthesised. Forest plots will be used to visualise the findings, and heterogeneity across the included studies will be assessed using the I² and Cochrane’s Q statistics. Potential publication bias will be assessed using a funnel plot along with the Begg’s and Egger’s tests. Sensitivity and subgroup analyses will also be conducted to assess the robustness of pooled estimates and to explore potential sources of heterogeneity. Statistical analysis will be conducted using Rstudio (v.4.3.2) and GraphPad Prism V.9.0.2.
The systematic review is focused on the analysis of secondary data from published literature; thus, no ethical approval will be needed. The protocol will follow international standard guidelines, findings will be reported in a reputed journal and disseminated through (inter)national conferences, webinars and key stakeholders to inform policy, research and AD management strategies.
CRD 420251047105.
Artificial Intelligence is revolutionizing healthcare by addressing complex challenges and enhancing patient care. AI technologies, such as machine learning, natural language processing, and predictive analytics, offer significant potential to impact nursing practice and patient outcomes.
This systematic review aims to assess the impact of Artificial Intelligence applications in healthcare on nursing practice and patient outcomes. The goal is to evaluate the effectiveness of these technologies in improving nursing efficiency and patient care and to identify areas requiring further research.
This review, conducted in August 2024, followed PRISMA guidelines. We searched PubMed, GOOGLE SCHOLAR, and Web of Science for studies published up to August 2024. The inclusion criteria were original research on AI in nursing and healthcare practice published in English. A two-stage screening process was used to select relevant studies, which were then analyzed for their impact on nursing practice and patient outcomes.
A total of 5975 studies were surveyed from the previously mentioned databases, which met the inclusion criteria. Findings show that AI applications, including machine learning, robotic process automation, and natural language processing, have improved diagnostic accuracy, patient management, and operational efficiency. Machine learning enhanced disease detection, reduced administrative tasks for nurses, NLP improved documentation accuracy, and physical robots increased patient safety and comfort. Challenges identified include data privacy concerns, integration into existing workflows, and methodological variability.
AI technologies have substantially improved nursing practice and patient outcomes. Addressing challenges related to data privacy and integration, as well as standardizing methodologies, is essential for optimizing AI's potential in healthcare. Further research is needed to explore the long-term impacts, cost-effectiveness, and ethical implications of Artificial Intelligence in this field.
Artificial Intelligence (AI) is revolutionizing healthcare by enhancing nursing practices and improving patient outcomes. Tools such as Clinical Decision Support Systems (CDSS), predictive analytics, robotic process automation (RPA), and remote monitoring empower nurses to make informed decisions, optimize workflows, and monitor patients more effectively. AI enhances decision-making, boosts efficiency, and facilitates personalized care, while aiding in early detection and real-time data analysis. It also contributes to better nurse education and patient safety by minimizing errors and enabling remote consultations. However, for AI to be successfully integrated into healthcare, it is essential to tackle challenges related to training, ethical considerations, and data privacy to guarantee its effective implementation and positive impact on the quality and safety of healthcare.
Registered nurses (RN)s account for the majority of the rural and remote health workforce and require different skills, knowledge and working practices compared to their metropolitan counterparts. Given the complexity and diversity of the rural and remote work environment, it is important to investigate the contemporary literature on the role and skill requirements of the RNs in these locations.
A scoping review was undertaken in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews. With the permission of the authors, this scoping review extends the work by Muirhead and Birks (2020) who explored the RN role in these locations in 2017. Database searches were conducted in the Cumulative Index for Allied Health and Nursing Literature (CINAHL), Cochrane, JBI, OVID (Emcare), Proquest, PubMed, Scopus and Rural and Remote Health Database. Studies published from November 2017 to June 2024 were included to reflect the current international roles of rural and remote RNs.
A total of 74 articles were included in the study. The overarching categories identified were clinical roles and non-clinical roles. Ongoing analysis established the subcategories of fundamental/foundational, specialist, management roles, support roles and ancillary roles. Four tensions within the rural and remote context were also identified; Generalist and specialist role; Poorly prepared or unprepared; Extended scope of practice; and Role uncertainty.
Registered Nurses in rural and remote locations conduct a wide variety of skills and tasks. Their role is expansive, context-dependant, and dynamic. Analysis of the literature found that globally, similarities exist for the role, including comparable challenges, barriers and opportunities Resource availability in a country impacts RN preparation, emphasising the need for systemic improvements to ensure equitable outcomes, especially in rural and remote areas.
The role of the rural and remote RN is broad and unique and requires different breadth and depth of skills and knowledge. The rural and remote RN role includes all levels of care for all patients across the lifespan, with varying resource and support levels. This scoping review provides valuable insight into the skills required to care for diverse communities. Understanding these requirements is essential, as it can inform the future focus on rural and remote nurse education and training and its subsequent impact on the quality of care for people living in rural and remote communities.