11237 research outputs found
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Evaluation of simulated task performance of a novel human factors inspired airway management equipment bag compared to an industry standard bag
No full textIntroduction: The importance of human factors science within healthcare is increasingly recognised, including during product design. This study assesses the performance of a human factors inspired airway management equipment bag (the StarRoll™) - designed to reduce cognitive load during emergency airway management - against an industry standard bag (SCRAM™), for the purpose of equipment preparation during emergency airway management.Methods: 32 participants prepared equipment for a simulated emergency tracheal intubation. Equipment preparation was checked against Difficulty Airway Society (UK) guidelines for omissions. Task duration was timed, video footage captured for ergonomical analysis, and a survey of task performance undertaken using an adapted version of the NASA Task Load Index in order to assess product functionality and user satisfaction.Results: Time to readiness for intubation was 115s for StarRoll™ versus 99 s for SCRAM™ (p Conclusion: The StarRoll™ bag was found to be favourable during simulated task performance with regards reported stress, effort and cognitive load, suggesting incorporation of human factors into product design can improve clinician experience.</p
Unravelling the complexity: a systematic review of complications and implications of adolescent pregnancy in India
No full textObjectives: Adolescent pregnancy is an important public health issue in low and middle- income countries. They are vulnerable to pregnancy- related complications with adverse fetal outcomes because of their immature and developing reproductive systems.Study design: This study is a systematic review of peer-reviewed articles focused on maternal and foetal complications related to adolescent pregnancy in India. The review adhered to the PRISMA 2020 guidelines throughout the process.Methods: A comprehensive search of peer-reviewed articles was conducted using PubMed, CINAHL Plus, Scopus, Web of Science, Ovid-Embase, and Medline. Articles were screened based on predefined inclusion and exclusion criteria. Following critical appraisal, thirteen studies were included in the review.Results: The findings reveal a higher incidence of anaemia, hypertensive disorders of pregnancy, preterm delivery, low babies, and NICU admissions among adolescent mothers. The incidence of adolescent pregnancies in India ranges from 6% to 18.3%. Among adolescent pregnancies, the elevated rates of preterm delivery and low birth weight were associated with increased incidence of foetal distress and NICU admission, underscoring the heightened clinical vulnerability of this population.Conclusions: The review concludes that adolescent pregnancy in India has significant negative health impacts on both mothers and babies. Preventative measures should be comprehensive and multi-faceted, including stringent enforcement of legal measures to prevent child marriage, raising awareness among adolescents and communities about pregnancy-related complications, and providing comprehensive, age-appropriate sexuality education.</p
Advancing skin cancer detection through deep learning and fusion of patient metadata and skin lesion images
No full textThere has been a significant rise in skin cancer incidence during the last three decades and the waiting time for skin lesion assessment in both the NHS and private sectors in the UK has increased significantly. Therefore, to reduce waiting time and to make a faster decision, there is a need to develop automated methods that can be used to classify whether a skin lesion is suspicious or non-suspicious during teledermatology triage. In this study, we propose an AI framework that uses patient metadata together with image data to classify skin lesions into suspicious or non-suspicious categories. To evaluate our proposed approach, we collected 79,246 skin lesion images along with their 22 meta-features such as lesion size, lesion colour, lesion shape, patient age, and gender from 19,295 patients who attended a network of private skin cancer diagnostic centres across the UK. We developed three separate models for skin lesion classification: (1) an AI model using only metadata that achieved 85.24 ± 2.20% sensitivity and 61.12 ± 0.90% specificity; (2) an AI model using only images that achieved 99.72 ± 1.35% sensitivity and 63.22 ± 3.11% specificity; and (3) a fused model based on both metadata and images that achieved 99.66 ± 0.28% sensitivity and 74.45 ± 0.80% specificity. The decisions of the developed AI models were then fused through a majority voting technique, which achieved a sensitivity of 99.50 ± 1.18% and a specificity of 82.72 ± 1.64%, significantly outperforming the state-of-the-art methods that rely solely on image data. Furthermore, we add a post-processing step to explain AI model decisions by implementing a soft-attention module that provides essential explainability and supports healthcare professionals in informed decision-making. The developed AI framework has great potential for the detection of suspicious skin lesions. With a reduction in patient referrals for possible biopsies, waiting times for skin cancer diagnosis and treatment will be shortened, resulting in improved outcomes.</p
The impact of study size on COVID-19 treatment outcomes: a meta-epidemiological study comparing large and small randomized controlled trials: a systematic review and meta-analyses
No full textSmall randomized controlled trials (RCTs) in COVID-19 meta-analyses have been associated with more favourable treatment effects and reduced result stability. This study assessed how trial size impacts effect estimates, statistical stability, and risk of bias. Following PRISMA guidelines, we identified meta-analyses of COVID-19 treatments included in WHO, NIH, and the LIVING Project. Trials were classified by log-scale sample size, and separate pooled meta-analyses were conducted for large-only, small-only, and combined trials. Comparative metrics included the Ratio of Odds Ratios (ROR), Kappa statistics, Fragility Index (FI), Reverse Fragility Index (RFI), and Cochrane Risk of Bias assessments. Sensitivity analyses applied alternative size thresholds (≥ 1000 participants and median-based cutoffs) and stratified results by treatment and outcome type. Across 25 meta-analyses including 221 RCTs (46 large, 175 small), small trials produced more extreme estimates in 19 analyses and wider confidence intervals in 23. The pooled ROR was 0.85 (95% CI: 0.76–0.95; P = 0.004), decreasing to 0.81 (95% CI: 0.68–0.95; P = 0.011) when limited to small trials published before the first large trial. RORs remained below 1 across treatment and outcome types. Agreement between small and large trials was minimal, while large trials showed substantial agreement with overall estimates. Stability and bias profiles favoured large trials (FI: 14.0 vs. 4.0; RFI: 10.0 vs. 5.0). In conclusion, small RCTs tend to overestimate treatment effects and yield less precise, less stable results. Meta-analyses should prioritise large, high-quality trials and interpret small-study findings with caution, particularly in rapidly evolving research contexts.</p
Young carers and inequalities in educational attainment and school engagement: Evidence from the UK household longitudinal study linked to the national pupil database
No full textYoung carers (individuals under 18 providing care to family members) experience significant disadvantages. While prior research suggests caring negatively impacts education, evidence is limited by methodological constraints and lacks national-level representation. This study aimed to assess associations between young caring and official educational attainment and school engagement at primary and secondary school levels in England, and to identify potential inequalities by gender, ethnicity, socioeconomic factors, household composition, and special educational needs. We used data from Understanding Society: UK Household Longitudinal Study linked with the National Pupil Database. We used cross-sectional pooled data covering 2009–2018, focusing on two educational stages in England: Key Stage 2 (KS2, end of primary school) and Key Stage4 (KS4, end of secondary school). Regression models assessed associations between self-reported young caring and educational outcomes (attainment and absenteeism), adjusting for sociodemographic covariates.Young carers made up 12.8% of the KS2 sample (n = 1740) and 10.6% of the KS4 sample (n = 2091). They had significantly lower attainment at KS2 (reading, mathematics, writing) and at KS4 (fewer and lower-grade GCSEs). Persistent absenteeism was substantially higher among young carers compared to non-carers (KS2: 5.8% vs 3.7%; KS4: 24.5% vs 19.1%). Socioeconomic disadvantage explained part, but not all, of the educational gaps. No additional inequalities were observed. These findings demonstrate that young carers face early and persistent educational disadvantages, with lower attainment and higher absence rates partially linked to socioeconomic inequality, highlighting the urgently need for target support to help young carers manage responsibilities and mitigate negative impacts on education.</p
Transport performance enhancement through risk-informed bridge scour management
No full textBridges are vital connections within transport networks, but scour-induced failures can severely disrupt network connectivity, increase user travel delays, and reduce reliability. The goal of this paper is to prioritize bridge scour management actions to improve transport network performance, defined here by connectivity and delay. This paper introduces a novel risk-informed decision-support framework that aids long-term programming and real-time operational decision processes. This framework couples bridge-level monitoring with network-level prioritization based on predicted transport-user impacts and early-warning triggers. It quantifies expected travel delays and network connectivity under different flood scenarios, guiding maintenance and protection investments toward bridges with the largest performance consequences. The framework is applied to a case study on UK railway bridges where warning times to failure are estimated and proactive bridge closures are simulated to assess operational impacts. The results inform the risk-aware prioritization of bridges for operational measures. This risk-informed approach extends traditional scour management by explicitly tying asset interventions to user-oriented performance outcomes and by supporting long-term programming and real-time operational decisions under uncertainty.</p
Occupational psychosocial risks as predictors of depression, anxiety, and stress among hospital employees
No full textWorkplace mental health is a growing concern in Malaysia’s healthcare sector, yet comprehensive psychosocial risk assessments across all staff remain limited. This cross-sectional study examined the prevalence and predictors of depression, anxiety, and stress among employees in four government tertiary hospitals in Kota Kinabalu, namely Hospital Queen Elizabeth, Hospital Queen Elizabeth II, Hospital Wanita dan Kanak-Kanak Sabah, and Hospital Mesra Bukit Padang. From 21
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March 2025–20
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April 2025, 233 staff members were selected via stratified random sampling. Data were collected using validated self-administered online questionnaires, including the 21-item Depression, Anxiety and Stress Scale and the Likelihood of Environment & Occupational Exposure Scale towards Psychosocial Risk in the Workplace. Analyses involved descriptive statistics, bivariate comparisons, and multivariate logistic regression using SPSS version 29. Results revealed high prevalence rates of anxiety (43.8%), depression (37.8%), and stress (27.0%). Bivariate analysis revealed elevated odds of depression among Chinese ethnicity, diploma educated, high-income staff, HQE employees, medical and clinical roles, doctors, and shift workers. Anxiety was linked to medical departments and shift work, while stress was prevalent in younger staff with shorter tenure. High job demand, low control, and inadequate support increased depression, anxiety, and stress risk. Multivariate analysis identified high psychosocial risks related to job demand (OR 3.94), control (OR 3.72), and support (OR 2.87) as significant predictors of depression. High psychosocial risk in job demand (OR 3.01), control (OR 2.29), and support (OR 2.59) also predicted anxiety. Stress was closely linked to staff aged 20–39 years (OR 3.14), high psychosocial risk in job control (OR 4.45), and support (OR 2.68). Although the cross-sectional design and reliance on self-report limit causal interpretation, these findings highlight the value of regular psychosocial risk assessments and targeted interventions. Strengthening workplace support systems is crucial to improving mental well-being among Malaysia’s hospital workforce.</p
Learning from the past: a scoping review of hospital disaster preparedness assessment
No full textBackground: Despite the recognized importance of hospital disaster preparedness (HDP), efforts to improve it have been limited. Improvement in HDP will need an in-depth comprehension of the prevailing gaps in addition to evidence of the best practices. However, a comprehensive review that synthesizes the findings from HDP assessments across diverse contexts and translates them into actionable insights is currently lacking. Considering these gaps, this scoping review aims to examine current practices, identify recurring gaps, consolidate best practices, and provide actionable recommendations through an in-depth literature review.Methods: A scoping review was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search strategy using keywords related to HDP was carried out in three databases: PubMed, Scopus, and Web of Science. Eligibility criteria were defined according to the population, intervention, comparison, and outcome. The search included studies from January 2015 to December 2024. Screening and data extraction were done by two independent reviewers, and the extracted data were subjected to a narrative synthesis. Data extraction and analysis were manually performed using Excel.Results: 46 eligible articles were identified from 10,656 records, the majority from Iran and Saudi Arabia. Cross-sectional studies dominated, and the majority utilized the hospital safety index tool. Two-thirds of hospitals reported a moderate level of preparedness. Substantial variability in hospital safety scores was observed, with structural safety ranging from 28% to 76.16%, nonstructural safety from 17.02% to 73.2%, and functional preparedness from 11.35% to 95%. Most hospitals lacked adequate structural safety, backup communication systems, proper safety measures for furniture and medical equipment, training programs, comprehensive emergency planning, staff welfare strategies, and adequate logistics and supplies.Conclusions: HDP should be viewed as an evolving, ongoing process, requiring a balanced HDP framework that addresses all aspects of preparedness, region-specific guidelines tailored to the unique needs and risks of the hospital, and context-driven interventions to enhance hospital resilience.</p
Modelling adipose tissue-cancer crosstalk: a three-dimensional perspective
No full textInnovative three-dimensional (3D) systems have become a focus of research due to their ability to better mimic cell-cell and cell-extracellular matrix interactions. Current advances in 3D modelling have the potential to transform pre-clinical research by providing a more biologically relevant recapitulation of the in vivo cell environment. Among the published 3D platforms there is a lack of adipose tissue and cancer complex models. Primarily thought to function in triglyceride storage, protection and heat production, adipose tissue is now recognised as a complex and dynamic endocrine organ that secretes factors such as free fatty acids and adipokines, which have been shown to play a role in carcinogenesis. Obesity, a major cause of adipose tissue dysfunction, has also been strongly linked to the development of several types of cancer. 3D model technologies offer an innovative way to investigate adipose tissue-cancer crosstalk by mimicking in vivo conditions. This review aims to present a perspective on the adipose tissue-cancer dynamics and provide an overview of the current 3D models used to reliably reproduce the adipose tissue-cancer interaction in vitro.</p
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