Public Health and Healthcare

Abstract: Objective: To synthesize evidence on phenotypic non-susceptibility, antimicrobial-resistance genes (ARGs), genetic mobility, transferability, and oral-resistome effects associated with oral-care probiotic strains and products. Design: A focused narrative search of PubMed, Google Scholar, publisher platforms, and reference lists was conducted through 3 August 2026, covering product-level, strain-safety, oral-biofilm/resistome, regulatory, and mechanistic studies; non-oral probiotic data were treated as indirect evidence. Results: A survey of eight commercial oral probiotic lozenges reported non-susceptibility phenotypes and PCR-detected ARG-like sequences without demonstrated transfer to selected bacterial recipients. Strain-level assessments of Streptococcus salivarius K12, DB-B5, G7, and M18, and of Weissella cibaria CMU/CMS1, generally did not identify acquired transferable determinants. Limosilactobacillus reuteri DSM 17938 illustrates risk mitigation: its parent strain's tet(W)/lnu(A) plasmids were removed by classical curing, and subsequent antibiotic-selected reduced erythromycin susceptibility was not linked to known transferable genes or transferred to Enterococcus faecalis. A three-dimensional oral biofilm study found a transient reduction in active ARG abundance during peak K12 colonization, with no increased putative mobilization. Transfer between non-oral probiotic donors and bacterial recipients has been demonstrated only after laboratory adaptation under high streptomycin pressure. Conclusions: No study identified in this review demonstrated donor-to-recipient ARG transfer from an oral-care probiotic during human use, though this absence of evidence should not be read as proof that transfer cannot occur. Several products, including the widely used ATCC PTA 5289, remain incompletely characterized, and long-term effects on the human oral resistome are unknown. Standardized whole-genome, phenotypic, mobility, finished-product, and longitudinal human assessments are needed.

Abstract: Objective: This study aimed to evaluate how using personal health records (PHR; Health2Sync^®) impact long-term glycemic management and body weight in people with type 2 diabetes (T2DM) and to identify factors associated with glycated hemoglobin (HbA1c) changes. Methods: This single-center, retrospective observational study included 154 patients with T2DM (76 PHR users and 78 non-users [control]; mean age: 57.9 and 65.0 years, respectively) between April 2022 and June 2024. Clinical parameters (HbA1c, body weight; BW), nutritional support, and antidiabetic medications were collected at the index date and at 3, 6, and 12 months. The between-group difference in HbA1c change (ΔHbA1c) from the index date to 12 months was the primary outcome, and that in BW change (ΔBW) was the secondary outcome. Factors associated with ΔHbA1c were explored using multivariate analysis in PHR group. Results: The PHR group showed a significantly greater reduction in HbA1c than the control group over 12 months (−0.6 ± 1.2 % vs. −0.1 ± 0.6 %; p = 0.005). Similarly, body weight was significantly reduced in the PHR group (−2.8 ± 3.8 kg vs. −0.5 ± 3.3 kg; p < 0.001). Moreover, multivariate regression analysis revealed an independent association between nutritional support and HbA1c improvement. Conclusions: PHR use was associated with improved glycemic control and weight reduction in people with T2DM, and together with nutritional support, it may further enhance self-management and clinical outcomes.

Abstract: Emerging infectious diseases continue to threaten global public health, veterinary medicine, agriculture, environmental security, and economic stability. Although artificial intelligence has significantly improved epidemic forecasting, most existing approaches remain reactive, focusing on predicting disease spread after outbreaks have already been detected. Consequently, current surveillance systems provide limited capability for identifying conditions associated with pathogen emergence and supporting proactive biological risk assessment.Unlike conventional forecasting systems that primarily respond to already established outbreaks, the proposed framework aims to identify upstream biological, environmental, and epidemiological conditions associated with pathogen emergence before sustained epidemic transmission occurs. By integrating heterogeneous One Health information, the framework aims to identify early biological risk signals and support preventive interventions prior to large-scale epidemic development.This paper presents Pandemic Radar AI, a prototype AI-based One Health framework designed to support early biorisk assessment, pandemic forecasting, and evidence-based decision-making through the integration of heterogeneous biological, environmental, and epidemiological information. Unlike conventional forecasting models, the proposed framework combines epidemiological surveillance, virological, veterinary, environmental, climatic, demographic, and geospatial data within a unified multimodal analytical architecture to identify conditions associated with pathogen emergence, identify biological risk hotspots, forecast epidemic dynamics, and generate decision-support indicators for public health authorities.The prototype implements a scalable modular architecture integrating machine learning, artificial neural networks, cellular automata, multimodal data fusion, semantic data integration, feature engineering, uncertainty-aware risk assessment, explainable artificial intelligence (XAI), and AI-assisted decision support. Artificial intelligence methods were selected because epidemic forecasting is characterized by high uncertainty, nonlinear dynamics, and heterogeneous multimodal data. Complementary AI approaches therefore enable more robust modelling of complex epidemiological processes than any individual algorithm alone. To demonstrate technical feasibility, a pilot forecasting module was developed and evaluated using publicly available SARS-CoV-2 epidemic data as a representative case study. The present study focuses on the conceptual framework and prototype implementation rather than on a complete computational description of all analytical modules. The proposed architecture is intended as a scalable foundation for future development and validation using multiple infectious diseases and heterogeneous One Health data sources.Preliminary experiments demonstrated reliable short-term forecasting using SARS-CoV-2 epidemic data. Forecasting performance gradually decreased with increasing prediction horizon, while useful predictive capability was maintained for horizons of up to 14 days. Maintaining useful predictive performance over a 14-day forecasting horizon is particularly important for public health preparedness because it provides additional time for surveillance, resource allocation, risk communication, and implementation of preventive interventions.The proposed framework extends conventional epidemic forecasting by introducing an integrated approach that links biological risk assessment, outbreak forecasting, and decision support within a single AI ecosystem. The framework is designed to support explainable and uncertainty-aware AI-assisted decision making by integrating heterogeneous One Health information into a unified analytical environment suitable for continuous biosurveillance. The presented prototype establishes a foundation for next-generation AI-supported biosurveillance systems capable of continuously integrating multimodal One Health information to identify biological risk conditions, identify conditions associated with pathogen emergence, extend practical epidemic forecasting horizons, and support preventive public health interventions before widespread epidemic transmission occurs. The proposed framework is designed as an evolving research platform that can be progressively expanded and validated across diverse emerging pathogens, geographical regions, and multimodal One Health data sources. The modular architecture also provides a foundation for future integration of geospatial Earth observation data and cybersecurity components to strengthen biosurveillance resilience, environmental monitoring, protection of critical public health infrastructures, and preparedness for emerging biological threats.

Abstract: A series of documented deaths by suicide following prolonged interactions with chatbots based on large language models (LLMs) has exposed the clinical risks of delegating emotional support to conversational artificial intelligence. This paper proposes a psychodynamic account of these events, integrating the bi-logical theory of Ignacio Matte Blanco, the Freudian theory of primary hallucination, and the analysis of demand elaborated by Carli and Paniccia. The suicidal crisis is conceptualized as a pathological prevalence of symmetric logic, marked by the generalization, maximization, and irradiation of psychic pain and by the collapse of temporality, while LLM-based chatbots are conceptualized as systems of pure formal asymmetry lacking any symmetric, emotional, and embodied base. Their encounter generates what we call relational hallucination: an unconscious, structurally determined process, homologous to primary hallucination, through which the subject in crisis invests the chatbot with relational qualities perceived as real. Because the process obeys the laws of symmetric logic, it cannot be corrected by information alone. The chatbot, responding to the explicit request rather than to the unconscious demand it conveys, produces an amplificatory collusion that reinforces the premises of the crisis. The failure of algorithmic support in acute suicidal states is therefore ontological rather than technical. Implications for clinical training, regulation, and research are discussed.

Abstract: Parents of autistic children frequently report elevated parenting stress, particularly stress related to child characteristics, and although parent-mediated interventions have been associated with reduced stress, findings remain mixed. This study examined changes in maternal parenting stress following a six-month early parent-mediated intervention and explored associations with child outcomes, maternal implementation of Naturalistic Developmental Behavioral Intervention (NDBI) strategies, baseline stress, and child age. Forty-five mother–child dyads (mean child age = 15.83 months) participated in a six-month, telehealth-delivered NDBI-based intervention. Maternal stress was assessed before and after treatment using the Parenting Stress Index–Fourth Edition (PSI-4). Wilcoxon signed-rank tests evaluated pre–post changes, and Spearman correlations examined associations with child developmental and autism outcomes, maternal NDBI implementation, baseline stress, and child age. Maternal stress decreased significantly in the PSI-4 Child Domain, particularly on the Acceptability/Distractibility and Reinforces Parent subscales. Greater reductions in Acceptability/Distractibility were associated with greater improvements in autism symptom severity (BOSCC) and developmental functioning (Griffiths-III). Higher baseline stress was associated with greater reductions across PSI-4 domains, whereas no associations emerged with maternal NDBI implementation. Early parent-mediated intervention is associated with reductions in maternal parenting stress, with greater reductions associated with improved child outcomes, supporting parenting stress as a relevant outcome of early autism intervention.

Abstract: Background/Objectives: The COVID-19 pandemic created demand for interhospital transfers of ill patients to balance intensive care capacity. Evidence on the safety, logistics, and outcomes of transfers in Germany remained limited. This study analyzed transport characteristics and factors influencing transfer quality and outcomes. Methods: This multicenter retrospective study included 414 interhospital intensive care transfers of adult patients with COVID-19 in Germany between January 2020 and June 2021. Data were extracted from transport reports and records. Primary outcomes were transport instability and clinical deterioration between takeover and handover. Secondary outcomes included the duration of transfer phases. Logistic regression identified factors associated with instability and deterioration, while linear regression assessed factors associated with transfer duration. Results: Most transfers were ground-based (92.5%). Mechanical ventilation was required in 75% of patients, catecholamines in 64%, extracorporeal membrane oxygenation (ECMO) in a small subset. Mean takeover, transport, and handover times were 53, 59, and 64 minutes, respectively. Transport instability occurred in 44% of patients, with multiple unstable events in 16%, while clinical deterioration occurred in 16.4%. Catecholamine therapy was associated with transport instability (odds ratio, 2.04; 95% CI, 1.30–3.19). Obesity and a P/F ratio below 100 mmHg were associated with clinical deterioration. Catecholamine therapy, ECMO, obesity, and mechanical ventilation prolonged transfer phases. Conclusions: Transport instability and clinical deterioration were common during interhospital transfers of critically ill patients with COVID-19. Catecholamine dependency, obesity, and severe hypoxemia were associated with adverse events. These findings support improved coordination, monitoring, staff training, and standardized documentation to enhance overall transport safety.

Abstract: Periodontitis is a highly prevalent chronic inflammatory disease characterized by dysbiosis of the subgingival microbiome and an altered host immune response, leading to progressive destruction of tooth-supporting tissues. Conventional diagnosis relies on clinical measurements and radiographic findings; however, emerging molecular and digital technologies are reshaping the diagnostic and therapeutic landscape of periodontology. This narrative review synthesizes recent literature on advances in periodontal diagnosis and therapy, focusing on molecular biomarkers, omics technologies, microbiome profiling, digital imaging, and artificial intelligence–based analytical models. Relevant studies were identified from major biomedical databases to provide an integrated and clinically oriented perspective on emerging diagnostic and therapeutic strategies. Evidence suggests that biomarkers from saliva and gingival crevicular fluid, together with omics approaches and microbiome characterization, may improve early detection and disease monitoring. Digital imaging technologies such as cone-beam computed tomography and three-dimensional reconstruction, combined with artificial intelligence and machine-learning algorithms, show potential to enhance diagnostic accuracy, disease classification, and risk prediction. These advances also support personalized treatment approaches, including host-modulation therapies, regenerative strategies, and digitally assisted treatment planning. The integration of clinical, molecular, and digital data supports the transition toward precision periodontology and more individualized patient management. Despite challenges related to biomarker validation, algorithm standardization, cost, and accessibility, these technologies may significantly improve diagnostic precision, prognostic assessment, and clinical decision-making. Continued research is needed to facilitate their validation and implementation in routine periodontal practice.

Abstract: Breastfeeding education in pre-professional healthcare programs, including dietetics, is often limited and inconsistent. To address this gap, an advanced breastfeeding education and lactation management curriculum was developed that combined evidence-based content with active-learning strategies to support the translation of knowledge into practice. Graduate dietetics students attended four instructional modules, two with hands-on learning activities, and completed pre-post surveys measuring breastfeeding knowledge, attitudes, and counseling self-efficacy. Open-ended questions allowed students to qualitatively evaluate the program. Learning gains were evaluated using 11 post-survey items, and thematic analysis of open-ended responses was conducted to identify recurring patterns and areas requiring additional instructional emphasis. Eighty-seven students (mean age = 24.67 ± 4.67 years; 94.3% female) from six cohorts completed the curriculum and evaluation. The gain items demonstrated high internal consistency (Cronbach’s α = .92). Across all 11 items, at least 48% of students reported good to great learning gains (Likert scores of 4 or 5). Qualitative findings indicated increased breastfeeding knowledge, greater appreciation of the complexity of lactation science, and enhanced awareness of challenges faced by breastfeeding mothers. The active-learning curriculum improved student learning and confidence in applying lactation education and counseling skills, providing a promising model for strengthening breastfeeding training in dietetics education and practice.

Abstract: Introduction: Co-administration is a safe and effective vaccination practice and is considered an important strategy for increasing vaccination coverage across populations. However, the real world uptake for respiratory co-administration is not well quantified. Objective: To better characterize rates of co-administration and potential missed opportunities. Methods: This retrospective study leveraged data from a large vaccine ecosystem that provides end-to-end vaccine management for more than 5,000 clinics located in 43 US states. Data were gathered from the vaccine ecosystem database for active patients, defined as those who were eligible for vaccination with at least one of three respiratory vaccines (COVID-19, influenza, and Respiratory syncytial virus [RSV]), had a clinic visit between July 1, 2022 and June 1, 2023, and received one or more of those vaccine(s) during the study period. Active patients were categorized according to the number and types of those vaccines received. Those receiving co-administration of vaccines were compared to those who did not receive co-administration. Results: Of the 1,233,349 patients who received at least one respiratory vaccine during the study period, 50,582 active patients (4.1%) received 2 or more co-administered vaccines. Middle-aged adults (51-64 years), women, commercially insured patients, and those vaccinated in primary care clinics and health departments were more likely to receive co-administered vaccines. The most commonly co-administered respiratory vaccines were influenza and COVID-19 (91.3%), followed by influenza and RSV (5.4%) and RSV and COVID-19 (2.5%). Only 0.7% of patients in the co-administration cohort received all 3 respiratory vaccines simultaneously. Conclusions: This study highlights substantial missed opportunities for co-administration of respiratory vaccines and identifies population- and clinic-level factors associated with higher uptake.

Abstract: Purpose: Reproductive coercion and abuse (RCA) is defined as deliberate interference in a person’s reproductive choices. It is overwhelmingly perpetrated against women by male intimate partners. Whilst it is generally agreed that RCA is harmful to health and wellbeing, reliable evidence for its specific associations has been lacking. In this study, our aim was to identify the physical, mental, sexual and reproductive health outcomes associated with RCA, using conceptually robust, comprehensive questions that encompass both pregnancy preventing and promoting behaviors and specify perceived perpetrator intent. Methods: In this study, we drew on data from two waves of the Australian Longitudinal Study on Women’s Health (n=6322 women aged 43-48 and n=5711 women aged 28-35). We reported the percentage of women with each health outcome, grouped by whether they had ever experienced RCA. We then estimated risk ratios for the association between RCA and each health outcome, controlling for sociodemographics and other intimate partner violence (IPV). Results: We found that lifetime partner-perpetrated RCA was associated with a range of adverse health outcomes for women aged 43-48 and 28-35 including elevated risk of unintended pregnancies (adjRR=2.42, 95% CI=2.16, 2.72 among women aged 28-35), miscarriages (adjRR=1.22, 95% CI=1.05, 1.41, and adjRR=2.08, 95% CI=1.74, 2.49, respectively), terminations (adjRR=2.39, 95% CI=2.11, 2.71, and adjRR=2.97, 95% CI=2.52, 3.50, respectively), premature birth (adjRR=1.72, 95% CI=1.01, 2.93 among women aged 28-35), and perinatal mental health disorders (adjRR=1.45, 95% CI=1.21, 1.74, and adjRR=1.68, 95% CI=1.42, 1.98, respectively), even after adjusting for sociodemographic variables and other IPV. RCA was also associated with poorer physical health (adjRR=1.33, 95% CI=1.05, 1.70, and adjRR=1.30, 95% CI=1.07, 1.57, respectively) and very high psychological distress (adjRR=1.24, 95% CI=1.03, 1.48 among women aged 28-35), including anxiety and depression (adjRR=1.24, 95% CI=1.07, 1.44, and adjRR=1.13, 95% CI=1.02, 1.24, respectively) and self-harm (adjRR=2.04, 95% CI=1.17, 3.56, and adjRR=1.49, 95% CI=1.11, 1.99, respectively). Conclusions: Our findings have important implications at the policy level and for the healthcare sector, highlighting the potential harms of RCA and the need for tailored screening, early intervention and response, particularly in abortion and antenatal settings.

Abstract: Issue addressed: High consumption of ultra-processed foods (UPFs) is linked to poor health outcomes, yet consumers often struggle to recognise and interpret food processing. Digital tools using artificial intelligence (AI) can support nutritional literacy and UPF awareness. This study evaluated a HISS (Human Interference Scoring System)–based mobile application designed to classify foods by processing level and support dietary self-reflection. Methods: A three-day quantitative usability study was conducted in New Zealand. Thirty-one participants (13 adolescents aged 12–18 years, eight tertiary students aged 19–25 years, and nine Māori and Pacific health coaches) logged all meals, snacks and beverages using the HISS app. AI classification accuracy was assessed against expert ratings of food images. App engagement was measured using in-app metrics, and usability and perceived impact were assessed via surveys. Results: The AI system achieved 93% accuracy for HISS category classification. App engagement varied across features, with most time spent on meal logging and AI interaction screens. Adolescents and health coaches reported high usability and usefulness, while tertiary students expressed more mixed intentions regarding ongoing use. Conclusions: The app demonstrated high classification accuracy and was generally well received, particularly among users with lower baseline nutrition literacy. Findings support the feasibility of using AI-enabled image recognition to support awareness of UPF intake. So what?: Evidence based AI food classification tools such as HISS show promise for scalable UPF reduction. With further development and evaluation, such tools may support nutrition education and behaviour change in community and clinical settings.

Abstract: Background: The Granularity-Related Inconsistency of Means (GRIM) test is a simple arithmetic method for detecting impossible means in integer-valued data. If observations are whole numbers and the sample size is known, a reported mean must be compatible with an integer total divided by that sample size. Purpose: This commentary summarises the assumptions required for valid GRIM use, explains borderline failures, and illustrates common misapplications using PubPeer and journal-debate examples. Conclusion: GRIM is strongest when applied to verified integer instruments, but it can mislead when used on age, continuous scales, composite scores, ambiguous sample sizes, or unclear rounding precision.

Abstract: Decades of progress in global sexual and reproductive health programming and data availability are threatened as over 80% of donor funding for family planning (FP) comes from governments that have announced significant cuts to Official Development Assistance. This commentary examines how FP2030 is adapting its Measurement Framework and annual reporting given uncertainty around the future of household surveys, such as the Demographic and Health Survey, and the loss of technical resources. FP2030 will pivot from reporting on 22 indicators to a “minimum set” of 15 priority indicators. FP2030 arrived at these recommendations through consensus built during a series of working meetings with its Performance Monitoring and Evidence Working Group (PME WG), a group of FP measurement experts. The 15 indicators leverage existing mathematical models and health management information systems (HMIS). Moreover, this commentary provides a complementary set of indicators that should be prioritized if new data collection opportunities arise. Ultimately, while FP2030 and the PME WG believe that models and HMIS data will become more essential, these tools cannot fully replace household surveys. Continued investment in FP data availability remains critical to ensuring the community can review data that protects the rights and needs of individuals through current programming.

Abstract: Multiple sclerosis (MS) is a chronic neurological disease whose diagnosis typically relies on resource-intensive and partly invasive procedures such as MRI and spinal fluid analysis. This study explores whether deep learning can be used to identify RBC-related changes in MS to help understand the disease process better and identify new treatment modalities. An augmented dataset of 1,944 images, comprising samples from 13 subjects (healthy controls and MS-affected), was used. Four deep learning architectures, VGG16, DenseNet201, MobileNetV2, and the Swin Transformer, were evaluated using subject-level Leave-One-Out Cross-Validation (LOOCV). The Swin Transformer achieved the highest mean accuracy of 73.53%, followed by the fully fine-tuned VGG16 at 71.02%, though a paired t-test indicated no statistically significant difference between them (p = 0.66). Explainable AI techniques (Grad-CAM and SWTformer-v1) revealed that VGG16 focused on broader structural patterns in cell groupings, while the Swin Transformer attended to more localized regions. These findings suggest that deep learning can detect subtle morphological patterns in RBCs associated with MS, and model interpretability shows that MS-related disease cues may reside in cell clustering rather than in individual cell morphology.

Abstract:

Background: Evaluating obesity among young adults requires precise anthropometric screening methods alongside a clear understanding of the psychosocial impact of this pathology. This study evaluated the predictive capacity of several biometric indicators—Body Fat Percentage (BFP), Waist-to-Height Ratio (WHtR), Body Adiposity Index (BAI), and Waist-to-Hip Ratio (WHR)—and psychosocial scales—Satisfaction with Life Scale (SWLS) and Multidimensional Scale of Perceived Social Support (MSPSS)—in identifying obesity established by Body Mass Index (BMI), stratified by sex and academic programs. Methods: A cross-sectional study was conducted on 445 university students. Statistical analyses included multiple comparisons (ANOVA) based on sex and academic program, multilinear regression equations to predict BMI, and Receiver Operating Characteristic (ROC) curve analysis to determine the Area Under the Curve (AUC). Optimal cut-off points were identified using the Youden index. Results: Multilinear regression revealed that WHtR was the strongest positive predictor for BMI across all academic programs: Biomedical Sciences (BS) (β = 1.725, t = 7.822, p < .001), Computer Science and Engineering (CSE) (β = 1.986, t = 11.172, p < .001), and Social Sciences and Physiotherapy (SSP) (β = 1.695, t = 11.259, p < .001). Conversely, WHR and BAI exhibited strong inverse relationships with BMI across BS, CSE, and SSP programs (p < .001). BFP was a significant but weaker positive predictor for BS (β = .142, p < .02) and CSE (β = .136, p < .02), but not for SSP (p = .24). Social support had a small effect on BMI in BS (p < .04) and CSE (p < .01), while SWLS was irrelevant across all programs. In sex-stratified ROC analysis, biometric parameters demonstrated excellent discrimination. BFP was the strongest predictor with an AUC of 1.000 for both sexes (cut-off: 24.38% for men, 35.43% for women). WHtR proved robust, outperforming WHR (AUC_Men = 0,980; AUC_Women = 0,969), with optimal cut-offs of 0.56 (men) and 0.53 (women). Conversely, MSPSS and SWLS showed no predictive capacity or statistical significance relative to obesity (p > .05, AUC ≈ 0.50). Conclusions: While modern anthropometric markers (BFP and WHtR) represent infallible and rapid clinical screening tools, the psychosocial dimension evolves independently of weight status. This demonstrates psychological resilience among the sampled youth and refutes the stereotype of automatic quality-of-life degradation in the context of obesity.

Abstract: Background:Mixed methods reviews are increasingly used to address complex healthcare and social research questions; however, their methodological diversity has led to fragmentation in terminology, design, and execution. This lack of clarity presents challenges for both methodological choice and reproducibility.Aim:To systematically map and critically examine the range of mixed methods review approaches, with a focus on their methodological characteristics, integration processes, and operational guidance.Methods:A scoping review was conducted to identify methodological papers and applied examples of mixed methods reviews. To be included, studies had to primarily identify instructional content on how to undertake a mixed methods review, including information on integration or synthesis of data. Data were extracted on review type, synthesis processes, integration mechanisms, data transformation, and use of frameworks or guidance tools. Findings were analysed using an extraction map which provided the basis for synthesis of information.Results:Ninety-two methodological contributions were identified. A wide range of review types were identified, including realist, meta-narrative, mixed methods systematic, integrative, framework, rapid, scoping and QCA-informed approaches. Mixed methods systematic reviews and framework synthesis approaches were the most identified approaches. Despite this diversity, four dominant integration mechanisms emerged: narrative, comparative, mapping-based, and mixed-evidence integration. Operational clarity varied substantially, with theory-driven and structured approaches (e.g., realist synthesis, QCA) demonstrating clearer procedural guidance than integrative and rapid approaches. Iterative processes and theoretical engagement were key differentiators between descriptive and explanatory review outputs.Conclusion:Mixed methods review approaches are characterised by both methodological richness and conceptual fragmentation. Greater emphasis is needed on standardising reporting, clarifying integration and transformation processes, and aligning methodological choices with review purpose. This review provides a structured framework to support methodological decision-making and enhance transparency in mixed methods evidence synthesis.

Abstract: Background: Anti-doping education in most national systems remains anchored to knowledge-transmission models that catalogue banned substances and sanctions but rarely address the organizational, social, and motivational architecture surrounding a young athlete's decision to dope. Whether a management-oriented curriculum — grounded in the resource-based view (RBV) and stakeholder theory — outperforms conventional, rule-based instruction is untested in a developing-nation setting with acute resource constraints. Methods: We conducted a two-arm, parallel-group randomized controlled trial with 321 elite athletes aged 13–19 (intervention n = 161; control n = 160) recruited across athletics, football, and swimming federations in Ethiopia. The intervention arm received a six-session, management-oriented curriculum built on RBV and stakeholder-theory principles; the control arm received a duration- and format-matched conventional WADA/NADA-style knowledge curriculum. Anti-doping knowledge, attitudinal alignment, and ethical/moral reasoning were assessed pre- and post-intervention. Analyses used repeated-measures ANOVA, a 2×2 (modality × gender) factorial ANOVA, and bootstrapped mediation modeling with Sobel tests. Results: The intervention group improved on knowledge by 45% (18.5→26.9, η² = .05), on attitudinal alignment by 30% (22.4→29.1, η² = .04), and on ethical reasoning by 35% (23.0→31.0, η² = .06), each significantly larger than control-group gains (all p < .001). Gains were present in both sexes but numerically larger for male athletes (+38% vs. +29% in ethical reasoning). Mediation analysis showed that anti-doping knowledge significantly channeled the intervention's effect onto both attitudes (indirect effect = 6.57, Sobel z = 9.50, p < .001) and ethical reasoning (indirect effect = 6.20, Sobel z = 8.95, p < .001), while a substantial direct effect persisted after accounting for the mediator. Conclusions: A management-based curriculum that builds athlete capability and engages the surrounding stakeholder network produces markedly larger and more durable gains in knowledge, attitudes, and ethical reasoning than conventional rule-recitation education, with cognitive mastery acting as a partial mediator rather than the sole mechanism of change. These findings support a shift in anti-doping education policy — particularly in resource-constrained federations — toward capability-building, stakeholder-engaged pedagogy.

Abstract: Background Current handwashing techniques used by healthcare and laboratory workers in the United Kingdom can create splash around the sink area, providing an environment for opportunistic pathogens. These microorganisms may transfer onto other surfaces, such as paper-towel dispensers and door handles, or into the air, increasing the risk for transmission and infection. This study investigated potential differences in sink-area contamination between the common UK National Health Service (NHS) handwashing procedure, a modified NHS procedure, and volunteers’ own procedures, incorporating two soap-dispenser locations. Method Twenty-nine volunteers were divided into three groups of ten, with one volunteer being part of two groups due to low volunteer numbers. Each group performed one of the three handwashing comparisons defined by procedure and soap-dispenser location. A fluorescent gel under UV light was used to visualise generated splashes in and around the sink area. The number of contamination events for each test was recorded and compared to determine statistical significance of contamination levels from the different handwashing procedures. Results A slight modification to the commonly used NHS handwashing procedure (collecting soap with one dry hand and collecting water with the other before proceeding through the handwashing technique) significantly reduced the amount of sink-area contamination irrespective of soap-dispenser location (P<0.01). Reductions were greatest between the modified NHS procedure and the standard NHS procedure, with smaller, non-significant reductions for volunteers’ own procedures. Conclusion Employing the modified NHS handwashing procedure reduced sink-area contamination, by minimising splashing during initial soap and water collection. Subsequently, this could reduce microbial transference onto readily contactable surfaces and fomites, thereby reducing the risk of occupational- and healthcare-associated infections.

Abstract: Medical students are exposed to substantial academic and psychological demands that may influence their well-being, lifestyle behaviors, and academic performance. This study aimed to examine the associations between smartphone-related anxiety (nomophobia), insomnia, psychological well-being, coping strategies, and academic achievement among medical students. A cross-sectional study was conducted among 216 students at the University of Split School of Medicine. Data was collected through an anonymous digital questionnaire created in Google Forms, assessing general information and including the International Physical Activity Questionnaire, Nomophobia questionnaire, Insomnia Severity Index, Medical Student Well-Being Index, and Brief COPE questionnaire. Academic performance was evaluated using students’ self-reported grade point averages (GPAs). There was a high prevalence of nomophobia and reduced well-being among participants. Nomophobia was positively associated with insomnia severity and poorer well-being (r=0.172, p=0.011, r=0.268, p< 0.001, respectively), while insomnia severity (r=-0.172, p=0.011) and avoidant coping strategies (r=-0.167, p=0.014) were negatively correlated with academic performance. The examined demographic, lifestyle, digital, and psychological variables explained a modest 9.0% of the variance in students’ GPAs, which is consistent with the multifactorial nature of academic achievement. These findings suggest that digital dependence, sleep disturbances, coping styles, and psychological well-being are interrelated factors that may influence academic functioning among medical students.

Abstract: The relationship between the consumption of wine and other alcoholic beverages and health remains one of the most debated topics in nutrition and public health. Although many national drinking guidelines draw on broadly similar epidemiological evidence, they often yield markedly different recommendations on alcohol consumption and risk. These differences are particularly evident in debates over moderate consumption, where conclusions about potential cardiovascular benefits, cancer risks, and overall health effects vary with how evidence is evaluated and translated into public health advice. This review examines the principal methodological frameworks used to develop drinking guidelines over the past two decades. Five broad approaches are identified: synthesis of observational evidence, quantitative disease-risk modelling, precautionary public health approaches, continuum-of-risk communication, and integrated absolute-risk frameworks. Using examples from the United States (U.S.), the United Kingdom (U.K.), the Netherlands, Canada and Australia, we examine the scientific assumptions, methodological choices and policy judgements that underpin each framework, and demonstrate how they influence both risk assessment and the resulting recommendations. Our analysis suggests that differences between national drinking guidelines stem not only from the interpretation of scientific evidence but also from fundamentally different conceptual approaches to assessing and communicating alcohol-related risk. Factors such as beverage type, drinking pattern, dietary context, lifestyle behaviours, and the selection of acceptable risk thresholds are incorporated to varying degrees across guideline methodologies, leading to divergent conclusions about moderate alcohol consumption. Drawing on the strengths of existing approaches, we propose an integrated framework for developing future drinking guidelines that separates scientific evidence from policy judgement, incorporates both lifetime and short-term risks, and communicates risk transparently within the broader context of diet and lifestyle. Such an approach provides a more coherent basis for improving health literacy and may assist policymakers and consumers in interpreting evidence on moderate alcohol consumption, including wine consumed as part of healthy dietary patterns.