头部公司

Objectives Low intervention uptake and contamination can dilute effects of cluster randomised trials (CRTs). We investigated the feasibility of digital fingerprints to assess intervention coverage and contamination in a CRT of community-based sexual and reproductive health services for youth (CHIEDZA).Methods 24 clusters in Zimbabwe were randomly allocated to intervention/control. In intervention clusters, CHIEDZA services were provided in community halls for 30 months. A population-based survey of youth aged 18–24 (700/cluster) was conducted to ascertain impact. Digital fingerprints were collected from service attendees and survey participants, and the datasets were linked to assess intervention coverage at population level in intervention clusters and contamination in control clusters. Multilevel logistic regression estimated the association of hall distance with service uptake.Results Between April 2019 and March 2022, 36 991 clients attended the CHIEDZA service and 99.9% used biometric registration. In the survey 13 675/17 682 (77.3%) participants completed biometric registration: 1182 refused, 1235 bypassed and 1590 could not register.CHIEDZA service coverage in the intervention clusters was 23.1% and contamination was 3.7%. Against biometric registration match, self-reported service attendance had 75.3% sensitivity (95% CI 73.1% to 77.5%) and 92.7% specificity (95% CI 92.0% to 93.4%). Odds of CHIEDZA service use reduced by 52% for every 1km distance (OR: 0.48 95% CI 0.44 to 0.54).Discussion Biometric identification was feasible and acceptable in a community setting without time pressure. In population-based surveys additional technological challenges emerged. Biometrics enabled good estimation of intervention coverage and validated self-reported data. Youth community services must overcome distance barriers.Conclusion Biometric identification is useful for assessment of CRT coverage and contamination.Trial registration number https://clinicaltrials.gov/study/NCT03719521.

Background: Ensuring a safe and sustainable blood supply presents a persistent challenge in geographically dispersed regions. This study aimed to describe the collaborative strategies employed by the North Lampung Blood Transfusion Unit (BTU), Indonesian Red Cross (IRC), and the Yayasan Relawan Donor Darah (YRDD) to improve voluntary blood donor mobilization and blood availability, and to evaluate the role of community-based and digital communication approaches within this collaboration. Methods: A descriptive mixed-methods design was used. Quantitative data were extracted from official BTU IRC North Lampung administrative records covering January 2023 to December 2024, comparing monthly blood collection volumes before (January–August 2023) and after (September 2023–December 2024) initiation of the collaboration. Qualitative data were collected through semi-structured interviews with BTU IRC officers and YRDD coordinators, observational notes from donor recruitment activities, and a review of internal organizational documentation. Thematic analysis was applied to qualitative data, with triangulation conducted across multiple data sources to enhance credibility. Results: Following the initiation of collaboration in September 2023, both the volume and consistency of blood collection increased markedly. The flagship '1,000 Donors in a Day' event on 16 September 2023 attracted 1,004 presenting donors and yielded 702 blood bags collected in a single day, a record achievement for North Lampung Regency. Over the subsequent 15 months, eight collaborative blood donation events were conducted, collectively yielding 3,082 presenting donors and 2,529 blood bags collected. Key strategies included blood-type-based WhatsApp Groups, social media outreach via Instagram and Facebook, personalized donor reminders, and community-based mobilization through YRDD volunteers. Annual blood collection in 2024 reached approximately 9,899 bags, representing progress toward, but remaining below, the regional safe threshold of approximately 12,000 bags per year. Conclusion: Community-based participatory approaches supported by digital communication tools may offer a replicable model for voluntary blood donor recruitment in geographically dispersed settings, subject to local adaptation, institutional commitment, and sustained volunteer engagement.

Introduction: COVID-19 pandemic has underscored critical deficits in global health preparedness. Traditional lecture-based teaching is frequently criticised for its limited effectiveness in practical skills required for real-world crisis response, creating significant "readiness gap". Hence, this study was done to evaluate perceived necessity and importance of innovative teaching-learning methods for the pandemic module amongst medical undergraduates. Methods: Qualitative needs assessment was conducted using an interpretive description approach to explore stakeholder perspectives regarding the existing pandemic management teaching module and public health preparedness training within the undergraduate Community Medicine curriculum. Three focus group discussions were held with 28 purposively sampled participants at tertiary care teaching hospital, comprising faculty from Community Medicine (n=10), medical students who had completed pandemic training (n=10), and students awaiting training (n=8). Data were audio-recorded, transcribed verbatim, and analysed using manual thematic analysis. Results: Analysis identified four central themes. First, profound theory-practice gap in existing teaching, inadequately preparing students for real-world scenarios. Second, simulation and gamification were widely endorsed as promising, engaging solutions for teaching complex skills like outbreak investigation. Third, significant implementation barriers were anticipated, including resource limitations (time, technology), faculty capacity gaps, and need for cultural contextualisation. Fourth, stakeholders advocated for fundamental shift in assessment strategy towards practical, formative methods like OSCEs and peer feedback to align with innovative teaching. Conclusion: This study provides clear, stakeholder-driven mandate for transforming pandemic module through experiential learning. While simulation and gamification are seen as viable solutions, successful integration requires structured approach addressing faculty development, resource allocation, co-design with stakeholders and realigned assessment framework.

The rapid evolution of digital healthcare systems has been significantly accelerated by the convergence of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). This study presents an advanced healthcare analytics framework leveraging a Convolutional Neural Network (CNN)-based algorithmic approach for intelligent diagnosis, real-time monitoring, and predictive analysis. The integration of IoT-enabled wearable devices facilitates continuous acquisition of physiological data, while AI and ML models enable efficient processing and interpretation of complex biomedical datasets. The proposed CNN-based architecture is designed to extract spatial and temporal features from heterogeneous healthcare data, including medical imaging and biosignals, thereby improving diagnostic accuracy and clinical decision-making. The framework emphasizes edge-cloud collaboration to ensure low latency, scalability, and efficient resource utilization. Furthermore, the model incorporates data preprocessing, feature extraction, classification, and predictive modules to support early disease detection and personalized treatment strategies. Experimental insights from existing studies indicate that CNN-driven healthcare systems achieve high predictive accuracy and reliability when deployed within IoT ecosystems. The proposed approach also addresses challenges such as data heterogeneity, real-time processing, and system interoperability. Overall, this research contributes to the development of intelligent, cost-effective, and scalable healthcare solutions capable of transforming traditional clinical practices into data-driven, patientcentric systems.