Background There is a growing body of evidence from discrete choice experiments related to telehealth. Discrete choice experiments offer valuable insights in informing the design and evaluation of telehealth services and supporting the telehealth implementation and policy. Objective This review aims to examine studies assessing consumer preferences for telehealth using discrete choice experiments. Methods A systematic review was conducted, searching five health and multidisciplinary databases from inception until 4 April 2024. Grey literature searches, hand-searching, and reference list checks were also performed. Results Of 2,832 studies screened, 52 met the inclusion criteria. Most studies were conducted from the patient perspective (n=47/52, 90%) and covered a wide range of populations and settings. Of the included studies eligible for quality assessment, 68% (n=23/34) received high-quality ratings, while others were assessed as moderate. Studies comparing face-to-face consultations with telehealth generally found a preference for face-to-face appointments. Telehealth is viewed more favourably if it can be offered at a lower cost, reduces wait times, and is part of a comprehensive care plan. Telehealth tends to be preferred by younger patients and clinicians, those digitally literate, and those with less severe or more private and stigmatised health conditions. Conclusion While face-to-face consultations are generally preferred, telehealth is viewed as advantageous if it reduces costs, shortens wait times, and is integrated into comprehensive care. The findings highlight the importance of considering these attributes when designing telehealth preference studies and informing the adoption and design of telehealth services.

Background: Artificial intelligence (AI) is increasingly integrated into health care, including psychiatry and psychology. In educational contexts, AI offers new possibilities for enhancing clinical reasoning, personalizing content delivery, and supporting professional development. Despite this emerging interest, a comprehensive understanding of how AI is currently used in mental health education, and the challenges associated with its adoption, remains limited. Objective: This scoping review aimed to identify and characterize current applications of AI in the teaching and learning of psychiatry and psychology. It also sought to document reported facilitators of and barriers to the integration of AI within educational contexts. Methods: A systematic search was conducted across 6 electronic databases (MEDLINE, PubMed, Embase, PsycINFO, EBM Reviews, and Google Scholar) from inception to October 2024. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Studies were included if they focused on psychiatry or psychology, described the use of an AI tool, and discussed at least 1 facilitator of or barrier to its use in education. Data were extracted on study characteristics, population, AI application, educational outcomes, facilitators, and barriers. Study quality was appraised using several design-appropriate tools. Results: From 6219 records, 10 (0.2%) studies met the inclusion criteria. Eight categories of AI applications were identified: clinical decision support, educational content creation, therapeutic tools and mental health monitoring, administrative and research assistance, natural language processing (NLP), program/policy development, students' study aid, and professional development. Key facilitators included the availability of AI tools, positive learner attitudes, digital infrastructure, and time-saving features. Barriers included limited AI training, ethical concerns, lack of digital literacy, algorithmic opacity, and insufficient curricular integration. The overall methodological quality of included studies was moderate to high. Conclusions: AI is being used across a range of educational functions in psychiatry and psychology, from clinical training to assessment and administrative support. Although the potential for enhancing learning outcomes is clear, its successful integration requires addressing ethical, technical, and pedagogical barriers. Future efforts should focus on AI literacy, faculty development, and institutional policies to guide responsible and effective use. This review underscores the importance of interdisciplinary collaboration to ensure the safe, equitable, and meaningful adoption of AI in mental health education.

Background: The global push to scale up telemedicine services is challenged by complex, multilevel, multifaceted implementation and a lack of consensus on what the evidence-based essential building blocks of implementation are. Objective: We aimed to evaluate the evidence base supporting telemedicine implementation knowledge tools; identify shared conceptual constructs and outliers; and formulate recommendations to guide the design, development, and optimization of telemedicine services. Methods: We conducted implementation research using a rapid umbrella review, that is, an overview of systematic reviews, in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In total, we searched 3 databases (PubMed, Web of Science, and Scopus) for studies focusing on telemedicine implementation frameworks, models, and tools, collectively referred to as "knowledge tools." Reviews meeting the operational definition of a systematically undertaken, secondary evidence synthesis, such as systematic and scoping reviews, and those published from January 2018 to May 2024 were considered. A meta-aggregative qualitative analysis was undertaken, comprising inductive thematic synthesis. Results: In total, 18 reviews were selected, encompassing 973 primary studies. Global perspectives were reflected in 61% (n=11) of the reviews, while 33% (n=6) focused on low- and middle-income country contexts. The primary research included in the reviews represented 63 countries, spanning the Americas, Europe, Africa, the Middle East, and Asia and the Pacific. Findings indicated substantial heterogeneity across the identified telemedicine implementation theories, models, and frameworks. However, following evidence synthesis, considerable convergence was observed, highlighting a state-of-the-art understanding of the essential requirements for a national telemedicine implementation ecosystem. These were categorized into "process" and "thematic" dimensions. Process dimensions included readiness and needs assessment, road map and planning, managing change, implementing telemedicine services, and continuous improvement and measuring performance. Thematic dimensions covered human and sociocultural aspects; organization, operations, management, and leadership; communication and coordination; policy, legal, and financial considerations; clinical health condition and quality of care; and the wider context. Conclusions: The findings of this study inform a pressing translational research knowledge gap in telemedicine implementation, hindering the implementation of high-quality, sustainable, and scalable telemedicine systems. The study contributes to building global consensus on the state of the art of key constructs in telemedicine implementation and recommends that future research focus on field-testing the evidence-based implementation tools to evaluate their usability and adaptability across diverse telemedicine contexts.

Objective: This scoping review investigates the potential of 6G technology in healthcare, particularly in smart city settings, focusing on its enhanced data capabilities, AI's role in healthcare optimization, infrastructure support, interoperability, quality standards, and privacy and security concerns. Patients and methods: The scoping review followed the Arksey and O'Malley framework, with Levac et al.'s methodological advancements. The review team searched academic databases like PubMed/Medline, SCOPUS, Embase, Web of Sciences, and IEEE Xplore. They also explored grey literature sources like Google Scholar, OpenGrey, and Web of Science Conference Proceedings. A search strategy was developed, and 145 studies were selected from an initial pool of 9835 records from 2010 to 2025. The review categorized 145 studies into three phases, focusing on deploying 6G technology in healthcare, the infrastructure required, and ethical considerations related to the technology's ethical implications. Result: Phase one focused on advancements like real-time imaging, performing medical procedures remotely, using predictive tools to analyze data, and providing care tailored to individual patients. Phase two examined how the next generation of wireless technology (6G) could interact with communication systems, including techniques to handle large amounts of data (massive MIMO) and using extremely high-frequency signals (terahertz communications) to transfer information faster. Phase three explored ethical concerns about applying 6G technology, such as systems that make decisions based on user intentions (intent-driven management) and organizing information around data-based designs (data-driven architecture). The review highlights how 6G technology could revolutionize patient care and medical services by enabling faster data transfers, reducing delays, increasing system capacity, and incorporating artificial intelligence. Conclusion: The scoping review shows the capability of the transformative potential of 6G technology, particularly in healthcare and urban development, emphasizing its enhanced data transfer speeds, reduced latency, and increased capacity that can significantly improve patient care through better remote monitoring, security, and telemedicine services. It stresses the vital role of policymakers in guiding the development of 6G infrastructure, ensuring effective spectrum allocation, and implementing robust security measures while addressing health and electromagnetic exposure concerns. Policymakers are urged to adopt security-by-design principles, adhere to international standards, and foster collaboration among academia, industry, and government to drive innovation and ensure the responsible deployment of 6G technology. By stimulating research and establishing clear performance metrics, they can facilitate continuous improvement and adaptation, ultimately benefiting society as a whole. The review concludes that strategic policy formulation is essential for maximizing the advantages of 6G technology, leading to more intelligent, productive, and sustainable societal frameworks.

Background: Health Technology Assessment (HTA) is a crucial tool for evaluating the worth and roles of health technologies, and providing evidence-based guidance for their adoption and use. Artificial intelligence (AI) can enhance HTA processes by improving data collection, analysis, and decision-making. This study aims to explore the opportunities and challenges of utilizing artificial intelligence (AI) in health technology assessment (HTA), with a specific focus on economic dimensions. By leveraging AI's capabilities, this research examines how innovative tools and methods can optimize economic evaluation frameworks and enhance decision-making processes within the HTA context. Methods: This study adopted Arksey and O'Malley's scoping review framework and conducted a systematic search in PubMed, Scopus, and Web of Science databases. It examined the benefits and challenges of AI integration into HTA, with a focus on economic dimensions. Findings: AI significantly enhances HTA outcomes by driving methodological advancements, improving utility, and fostering healthcare innovation. It enables comprehensive assessments through robust data systems and databases. However, ethical considerations such as biases, transparency, and accountability emphasize the need for deliberate planning and policymaking to ensure responsible integration within the HTA framework. Conclusion: AI applications in HTA have significant potential to enhance health outcomes and decision-making processes. However, the development of robust data management strategies and regulatory frameworks is essential to ensure effective and ethical implementation. Future research should prioritize the establishment of comprehensive frameworks for AI integration, fostering collaboration among stakeholders, and improving data quality and accessibility on an ongoing basis.

Objectives: Considering the growing inter of biobanks, artificial intelligence (AI) and omics research, and their critical impact on public health, this study aimed to explore the current and future public health implications and challenges of AI and omics-driven innovations in biobanking. Study design: Narrative literature review. Methods: A structured literature search was conducted in Scopus, PubMed, Web of Science and IEEExplore databases using relevant search terms. Additional references were identified through backward and forward citation chaining. Key themes were aggregated and analysed through thematic analysis. Results: Thirty-seven studies were selected for analysis, leading to the identification and categorisation of key developments. Several key technical, ethical and implementation challenges were also identified, including AI model selection, data accessibility, variability and quality issues, lack of robust and standardised validation methods, explainability, accountability, lack of transparency, algorithmic bias, privacy, security and fairness issues, and governance model selection. Based on these results, potential future scenarios of AI and omics integration in biobanking and their related public health implications were considered. Conclusions: While AI and omics-driven innovations in biobanking offer specific transformative public health benefits, addressing their technical, ethical and implementation challenges is crucial. Robust regulatory frameworks, feasible governance models, access to quality data, interdisciplinary collaboration, and transparent and validated AI systems are essential to maximise benefits and mitigate risks. Further research and policy development are needed to support the responsible integration of these technologies in biobanking and public health.

Background: Electrophysiology (EP) procedures, including cardiac implantable electronic devices (CIEDs) and ablations, are widely used to manage arrhythmias and heart failure. These interventions, though effective, require substantial resources, prompting the need for systematic economic evaluations to inform healthcare decision-making. Methods: A systematic review of studies from 2007 to 2024 was conducted in two phases. Phase one assessed trends in economic evaluations of EP procedures, analyzing 129 studies across regions and timeframes. Phase two focused on cost-effectiveness analyses of implantable cardioverter defibrillators (ICDs), cardiac resynchronization therapy defibrillators (CRT-Ds), and atrial fibrillation (AF) ablation, examining outcomes like quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs), while identifying factors influencing economic results. Results: EP procedures generally demonstrated favorable cost-effectiveness, particularly in high-income regions. Studies on ICDs and CRT-Ds consistently supported their economic value for patients with arrhythmias or heart failure, while AF ablation showed potential for long-term benefits, particularly when compared to medical therapies. However, results varied by region, reflecting differences in healthcare systems, costs, and patient populations. Conclusions: The review highlights the overall cost-effectiveness of EP procedures in many settings but underscores the need for tailored economic evaluations in low- and middle-income countries. Simplified methodologies and greater attention to regional contexts are recommended to guide resource allocation and policy development globally.

Objectives: The rapid integration of artificial intelligence (AI) in healthcare requires robust legal safeguards to ensure safety, privacy and non-discrimination, crucial for maintaining trust. Yet, unaddressed differences in disciplinary perspectives and priorities risk impeding effective reform. This study uncovers convergences and divergences in disciplinary comprehension, prioritisation and proposed solutions to legal issues with health-AI, providing law and policymaking guidance. Methods: Employing a scoping review methodology, we searched MEDLINE (Ovid), EMBASE (Ovid), HeinOnline Law Journal Library, Index to Foreign Legal Periodicals (HeinOnline), Index to Legal Periodicals and Books (EBSCOhost), Web of Science (Core Collection), Scopus and IEEE Xplore, identifying legal issue discussions published, in English or French, from January 2012 to July 2021. Of 18 168 screened studies, 432 were included for data extraction and analysis. We mapped the legal concerns and solutions discussed by authors in medicine, law, nursing, pharmacy, other healthcare professions, public health, computer science and engineering, revealing where they agree and disagree in their understanding, prioritisation and response to legal concerns. Results: Critical disciplinary differences were evident in both the frequency and nature of discussions of legal issues and potential solutions. Notably, innovators in computer science and engineering exhibited minimal engagement with legal issues. Authors in law and medicine frequently contributed but prioritised different legal issues and proposed different solutions. Discussion and conclusion: Differing perspectives regarding law reform priorities and solutions jeopardise the progress of health AI development. We need inclusive, interdisciplinary dialogues concerning the risks and trade-offs associated with various solutions to ensure optimal law and policy reform.

India's healthcare system faces substantial challenges, including a high burden of communicable and non-communicable diseases, limited access to healthcare in rural areas, and a shortage of skilled healthcare professionals. Artificial intelligence (AI) offers promising solutions to address these gaps by enhancing diagnostic accuracy, improving disease prediction, and optimizing treatment management. This scoping review examines AI's role in early detection, treatment, and disease prevention in community health settings. A comprehensive literature search was conducted in PubMed, Embase, Scopus, and Google Scholar from January 2013 to July 2024. Eligible studies focused on the application of AI in public health, emphasizing early detection, disease prevention, and treatment interventions. Data on AI models, health outcomes, and performance metrics were extracted and analyzed in line with PRISMA-ScR guidelines. Forty-eight studies were analyzed and categorized into diagnostic accuracy, disease prediction, treatment management, and clinical validation. AI-based tools, such as AIDMAN for malaria detection, demonstrated high diagnostic accuracy (95%) and AUC (0.96). Predictive models for chronic kidney disease (93% accuracy) and diabetes (91% accuracy) showed substantial promise. TB screening using AI-powered cough analysis achieved 86% accuracy. The studies also emphasized AI's role in managing chronic diseases, facilitating early interventions, and reducing healthcare burdens in resource-limited settings. AI has the potential to revolutionize healthcare delivery in India, particularly in underserved regions, by enhancing early detection and treatment. However, challenges related to data privacy, algorithmic bias, and infrastructure require attention. Continued research and policy development are essential to fully harness AI's capabilities in improving public health outcomes.

Background: Health Impact Assessment (HIA) procedures can include the assessment of inequalities and inequities associated with the distribution of environmental health risks and benefits, aimed at attenuating the exacerbation of environmental health disparities. This systematic review, conducted as part of the Joint Action Prevent Non-Communicable Diseases initiative, explores methods for assessing health inequalities and equity within HIA frameworks, particularly in local projects affecting the distribution of environmental risks and benefits. Methods: Adhering to the PRISMA guidelines, a systematic review of the scientific literature was conducted using the MEDLINE/PubMed, Scopus, and Embase databases, searching until March 8, 2024. Furthermore, a grey literature analysis encompassed the Institutional Repository for Information Sharing (IRIS) of the World Health Organization, to identify guidelines and recommendations addressing equity considerations in HIAs. Studies were included based on predefined eligibility criteria if they explored issues related to inequalities, inequities, and vulnerabilities within the context of HIAs. Data extraction focused on methodologies that incorporated equity considerations within the HIA framework, particularly concerning local urban planning initiatives, transport infrastructure, and industrial settings. Results: A total of 33 studies met the inclusion criteria. Among these, eight documents from the grey literature, identified as guidelines and guidance, underscored the importance of prioritizing equity to ensure that health impacts are addressed fairly across diverse population groups. The remaining 25 peer-reviewed studies employed a combination of quantitative and qualitative methodologies. Quantitative approaches, including exposure-response modeling and Geographic Information System (GIS) mapping, were utilized to evaluate spatial and demographic health disparities. Qualitative methods, such as focus groups, interviews, and participatory tools, provided insights into the lived experiences of vulnerable populations affected by local interventions. Studies addressing urban and transportation planning predominantly emphasized socioeconomic stratification, whereas those focused on industrial settings highlighted occupational hazards and community vulnerabilities. Conclusion: This review highlights the diverse and fragmented approaches used to address health inequalities and equity in HIA. It underscores the need for interdisciplinary and systematic methodologies that integrate quantitative and qualitative perspectives, ensuring equity remains a central consideration in policymaking and project implementation. Finally, it proposes a practical framework for integrating equity into HIA.

Artificial intelligence (AI) is increasingly being used in the field of medicine and healthcare. However, there are no articles specifically examining ethical and moral dimensions of AI use for evidence synthesis. This article attempts to fills this gap. In doing so, I deploy in written form, what in Bengali philosophy and culture, is the Adda (আড্ডা) approach, a form of oral exchange, which involves deep but conversational style discussion. Adda developed as a form of intellectual resistance against the cultural hegemony of British Imperialism and entails asking provocative question to encourage critical discourse.The raison d'être for using AI is that it would enhance efficiency in the conduct of evidence synthesis, thus leading to greater evidence uptake. I question whether assuming so without any empirical evidence is ethical. I then examine the challenges posed by the lack of moral agency of AI; the issue of bias and discrimination being amplified through AI driven evidence synthesis; ethical and moral dimensions of epistemic (knowledge-related) uncertainty on AI; impact of knowledge systems (training of future scientists, and epistemic conformity), and the need for looking at ethical and moral dimensions beyond technical evaluation of AI models. I then discuss ethical and moral responsibilities of government, multi-laterals, research institutions and funders in regulating and having an oversight role in development, validation, and conduct of evidence synthesis. I argue that industry self-regulation for responsible use of AI is unlikely to address ethical and moral concerns, and that there is a need to develop legal frameworks, ethics codes, and of bringing such work within the ambit of institutional ethics committees to enable appreciation of the complexities around use of AI for evidence synthesis, mitigate against moral hazards, and ensure that evidence synthesis leads to improvement of health of individuals, nations and societies.

Background: Corporate wellness programs are increasingly using digital technologies to promote employee health. Digital wellness programs (DWPs) refer to initiatives that deliver health interventions through digital tools. Despite a growing body of evidence on DWPs, the literature remains fragmented across multiple health domains. Objective: This study aims to provide a comprehensive synthesis of existing research on the efficacy (eg, impact on employee's physical health, mental well-being, behavioral changes, and absenteeism) and acceptability (eg, engagement, perceived usefulness, and adoption) of employer-provided DWPs. Specifically, we aim to map the extent, range, and nature of research on this topic; summarize key findings; identify gaps; and facilitate knowledge dissemination. Methods: We conducted a meta-review of studies published between 2000 and 2023. We adopted a database-driven search approach, including the MEDLINE, PsycINFO, ProQuest Central, and Web of Science Core Collection databases. The inclusion criteria consisted of (1) review articles; (2) publications in English, French, or German; (3) studies reporting on digital health interventions implemented in organizations; (4) studies reporting on nonclinical or preclinical employee populations; and (5) studies assessing the efficacy and acceptability of employer-provided DWPs. We performed a descriptive numerical summary and thematic analysis of the included studies. Results: Out of 593 nonduplicate studies screened, 29 met the inclusion criteria. The most investigated health domains included mental health (n=19), physical activity (n=8), weight management (n=6), unhealthy behavior change (n=4), and sleep management (n=2). In total, 24 reviews focused on the efficacy of DWPs, primarily in relation to health-related outcomes (eg, stress and weight), while fewer reviews addressed organization-related outcomes (eg, burnout and absenteeism). Four reviews explored the mechanisms of action, and 3 assessed the acceptability of DWPs using various measures. Overall, the findings support the efficacy and acceptability of DWPs, although significant gaps persist, particularly regarding the durability of outcomes, the role of technology, and the causal mechanisms underlying behavioral change. Conclusions: While DWPs show promise across a variety of health domains, several aspects of their effectiveness remain underexplored. Practitioners should capitalize on existing evidence of successful DWPs while acknowledging the limitations in the literature.

Background: Sickle cell disease (SCD) and Diamond-Blackfan anemia syndrome (DBAS) are two hereditary blood diseases that present significant challenges to patients, their caregivers, and the healthcare system. Both conditions cause severe health complications and have limited treatment options, leaving many individuals without access to curative therapies like hematopoietic stem cell transplantation. Recent advancements in gene and cell therapies offer the potential for a new curative option, marking a pivotal shift in the management of these debilitating diseases. However, the implementation of these therapies necessitates a nuanced understanding of the ethical and social implications. Methods: In this mixed methods systematic review, we explore the responsible development and implementation of gene and cell therapies for SCD and DBAS and aim to sketch a path toward ethically and socially sound implementation. Drawing upon principles of Responsible Research & Innovation and the 4A framework of availability, accessibility, acceptability, and affordability, we thematically analyze existing research to illuminate the ethical and social dimensions of these therapies. Following established PRISMA and JBI Manual guidelines, a search across multiple databases yielded 51 peer-reviewed studies with publication dates ranging from 1991 to 2023. Results: Our thematic analysis shows that the theme of acceptability is heavily shaped by interactions between patients, caregivers, healthcare professionals and researchers, influencing treatment decisions and shaping the development of curative gene and cell therapies. Despite the generally positive perspective on these therapies, factors like the limited treatment options, financial constraints, healthcare professional attitudes, and (historical) mistrust can impede stakeholder decision-making. While acceptability focuses on individual decisions, the themes of availability, accessibility, and affordability are interconnected and primarily driven by healthcare systems, where high research and development costs, commercialization and a lack of transparency challenge equitable access to these therapies. This diminishes the acceptability for patients, revealing a complex interdependence of the themes. Conclusions: The findings suggest the need for improved communication strategies in clinical practice to facilitate informed decision-making for patients and caregivers. Policy development should focus on addressing pricing disparities and promoting international collaboration to ensure equitable access to therapies. This review has been pre-registered in PROSPERO under registration number CRD42023474305.

Introduction: There is no recent consensus on the effectiveness of teledentistry versus in-person examination in the diagnosis of dental caries, especially after the COVID-19 pandemic. Objective: To assess the diagnostic accuracy of teledentistry versus in-person examination for dental caries diagnosis (PROSPERO #CRD42023410962). Methods: This systematic review and meta-analysis compared the effectiveness of teledentistry versus in-person examination for dental caries diagnosis. The eligibility criteria were peer-reviewed studies published in English between January 2013 and December 2021 that reported diagnostic parameters (specificity and sensitivity) for caries detection in primary and permanent dentition. Articles were extracted using search strategies from PubMed and CINAHL databases and screened using PRISMA-DTA guidelines, following a review for quality assessment and risk of bias using the QUADAS-2 and JBI Critical Appraisal Checklists. Meta-analysis was conducted in R using the MADA package. A descriptive analysis of the sensitivity, specificity, diagnostic odds ratio, and confidence intervals was performed with respective forest plots. Heterogeneity was assessed using Cochrane and Higgins's 2 tests. Univariate measures of diagnostic accuracy were performed based on the DerSimonian-Laird random effect and reported summary diagnostic odds ratios. Results: Twelve studies met the inclusion criteria and were reviewed and included in the meta-analysis. The diagnostic parameters ranged from 45.6% to 88.3% for sensitivity, 55.2% to 98.3% for specificity, 79% to 92% for positive predictive value, 48% to 97% for negative predictive value, and 70% to 96% for accuracy. The κ scores ranged from 0.46 to 0.89 for teledentistry modalities. Tests for equality of sensitivities and specificities were significant ( < 0.001). The studies were not heterogeneous with Cochran's : 14.502 ( = 0.206) and Higgins's 2 of 24%. The multivariable analysis showed a diagnostic odds ratio based on the DerSimonian-Laird random effect of 35.14, which indicates that the odds of caries detection via teledentistry is 35 times more true positive (i.e., correctly identifying a positive condition) than false positive. Conclusions: Diagnosis of caries via teledentistry is effective and comparable to in-person diagnosis. Remote assessments are consistent in diagnostic accuracy for caries.Knowledge Transfer Statement:This systematic review and meta-analysis added to the evidence about using teledentistry assessment as a diagnostically accurate tool to detect dental caries. Using teledentistry dental practices could promote greater access to dental and oral health care in the absence of in-person assessment.

Hysterectomy, removal of the uterus, is a commonly performed surgery for gynaecological morbidities. Emerging evidence indicates that hysterectomy performed before age 45 (early hysterectomy), is associated with considerable risks to women's health. While most evidence on hysterectomy is from high-income settings, national surveys from India report high prevalence of early hysterectomy in specific regions, as well as higher prevalence amongst women in rural areas and with less education. The median age at hysterectomy in India is close to ten years before the onset of natural menopause. India has recently introduced national guidelines to address early hysterectomy, but large evidence gaps on the causes and consequences remain - which in turn limits the potential effectiveness of interventions at the clinical, health system and community level. Methods: SAHELI is a Team Science study that will examine: (i) individual, social and health system determinants of early hysterectomy; (ii) women's treatment pathways to hysterectomy and for gynaecological morbidity in general; and (iii) the consequences of undergoing hysterectomy on women's physical, mental, economic and social well-being across the life course. This mixed-methods study includes population surveys amongst women in ages 25-49 in three high-prevalence states; qualitative health systems research to trace treatment journeys with women, health care providers and other stakeholders; evidence syntheses; and knowledge translation activities to ensure findings inform co-produced strategies and interventions. The study is grounded in a feminist epidemiology approach, aiming to examine individual and structural causes of vulnerability and prioritising the views of women, particularly in knowledge translation. Conclusions: SAHELI, implemented by an all-women, multi-disciplinary team, is the first study in India to examine the causes and consequences of hysterectomy in a life course approach. We aim to influence interventions, policy and future research on women's health, particularly access to quality gynaecological care and comprehensive health services through the life course.

Purpose: To perform a scoping review of the literature investigating the use of telemedicine in retinoblastoma (RB) care, as well as its integration into broader health-care systems worldwide. Methods: A comprehensive search of academic databases, Medline and Embase, as well as public, web-based sources, was performed. Article references were also screened for inclusion within the review. Only English-language sources that presented evidence of dedicated frameworks incorporating telemedicine-based RB care were included. Results: The review identified 57 peer-reviewed studies, of which 19 were found potentially eligible for inclusion. Following exclusion criteria and a web-based search, 11 sources referencing 10 telemedicine frameworks, comprising four peer-reviewed articles, and seven publicly available initiatives, were included. Major themes leveraging telemedicine in RB care included knowledge exchange and capacity building, the establishment of direct partnerships incorporating teleconsultation, and the development of care coordination networks. Many telehealth platforms were effectively integrated into health-care systems; however, challenges included the absence of RB specific initiatives, the need for publicly available, measurable peer-reviewed outcomes and the complexities of multidimensional RB care. Conclusion: Tele-efforts have the potential to enhance delivery of RB care to remote, resource-limited communities. Future research should explore improved integration of policy-driven telemedicine within health-care systems, assess the economic feasibility of providing such care, and facilitate empirical evaluations, ultimately leading to increased sustainability, funding and investment of these models.

目的：系统综述离散选择实验(discrete choice experiment，DCE)在卫生人员工作偏好研究中的应用，提供使用DCE方法调查卫生人员工作偏好的参考，并提出激励卫生人才队伍高质量发展的建议。方法：本研究共纳入26篇国内外相关文献，通过系统综述分析DCE在卫生人员工作偏好中的应用方式及分析结果，并对工作属性排名、支付意愿、情景模拟等结果进行统计分析。结果：通过系统综述发现国内外的研究时间处于动态变化，研究对象多集中于发展中国家；相较于国外，国内研究目的更集中在探究激励机制和提高卫生人员稳定性上；大部分研究均采用了多种分析模型，包括混合Logit、潜变量Logit模型等。国内外研究中所设置的工作属性存在差异，国内外研究均表明工作环境和工资奖金是关键属性。结论：建议政策制定者和管理者应重视工作环境、工作时长、地点、社会认同和编制等多元因素，结合实际制定相关政策制度，以促进国内卫生人才队伍建设的高质量发展。

Introduction: Advances in therapeutics and healthcare have led to a growing population of individuals living with multimorbidity and polypharmacy making prescribing more challenging. Most prescribing occurs in primary care and General Practitioners (GPs) have expressed interest in comparative feedback on their prescribing performance. Clinical decision support systems (CDSS) and audit and feedback interventions have shown some impact, but changes are often short-lived. Interactive dashboards, a novel approach integrating CDSS and audit and feedback elements, offer longitudinal updated data outside clinical encounters. This systematic review aims to explore the effectiveness of interactive dashboards on prescribing-related outcomes in primary care and examine the characteristics of these dashboards. Methods: This protocol was prospectively registered on PROSPERO (CRD42023481475) and reported in line with PRISMA-P guidelines. Searches of PubMed, EMBASE, Medline, PsychINFO, CINAHL, Scopus, the Cochrane Library, and grey literature, including trial registries were performed to identify interventional studies (randomised and non-randomised) that assess the effectiveness of interactive dashboards on prescribing related outcomes. The search will be supplemented by searching references of retrieved articles with the use of an automated citation chaser. Identified records will be screened independently by two reviewers and data from eligible studies extracted using a purposely developed data extraction tool. We will narratively summarise the intervention types and those associated with improvements in prescribing outcomes. A quantitative synthesis will be carried out if a sufficient number of homogenous studies are identified. Methodological quality will be assessed by two reviewers using the Cochrane Effective Practice and Organisation of Care risk assessment tool. Discussion: This systematic review will explore the effect of interactive dashboards on prescribing related outcome measures in primary care and describe the characteristics of interactive dashboards. This research may inform future intervention development and shape policymaking particularly in the context of ongoing and planned developments in e-prescribing infrastructure.

Background: The Trans-theoretical Model (TTM) has been applied in numerous empirical studies concerning physical activity (PA) interventions for children and adolescents. Consequently, the aim of this review is to identify and synthesize the evidence regarding the effectiveness of TTM-based interventions in promoting PA behavior change among this demographic, with the goal of informing future research and policy development. Methods: A systematic review was performed followed the PRISMA guideline, protocol was registered in PROSPERO (CRD42023416216). Computer-based searches were conducted in the CNKI, Wan-Fang, VIP, Web of Science (WOS), PubMed, and EBSCO databases to identify relevant literature. Two researchers independently conducted the literature screening and quality assessment. The quality of the randomized controlled trials (RCTs) was evaluated using the Risk of Bias Assessment Tool version 5.1.0, as recommended by the Cochrane Collaboration Network. For quality assessment of quasi experiments (QEs), the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool was employed. Results: A total of 22 articles were included in the systematic review. Stage-matched interventions and interventions designed based on a more complete structure of the TTM are more likely to promote an increase in the actual levels of PA among children and adolescents, as well as to facilitate an increase in their PA stages. Interventions that combine health information and health behavior feedback are more likely to promote an increase in actual PA levels and the advancement of PA stages; while interventions that include PA programs are more likely to facilitate improvements in health indicators. Conclusion: The effectiveness of TTM-based PA behavior change interventions for children and adolescents depends on the specific measures employed. Interventions that are stage-matched and integrate multiple behavior change techniques using the complete TTM structure are more likely to enhance PA and its associated health benefits. However, there are several normative issues that remain. These include the misuse of incremental stages as a substitute for PA, neglect of stage specificity when applying the model, a lack of framework for behavior change techniques in targeted interventions, and an absence of a dynamic feedback process.

Background and objective: Robotic-assisted radical prostatectomy (RARP) is a surgical option for localized prostate cancer. Cost-effectiveness analysis (CEA) findings are inconsistent when comparing it with open (ORP) and laparoscopic (LRP) radical prostatectomy approaches. We performed a systematic review and meta-analysis to pool the incremental net benefit (INB) of these approaches. Methods: Relevant CEA studies of RARP were identified by searching the PubMed, Embase, Scopus, International Health Technology Assessment database, Tufts CEA Registry, and Centre for Reviews and Dissemination databases from January 2005 to October 2023. To be included, studies must compare costs, and quality-adjusted life years (QALYs) of RARP versus ORP or LRP, and report the incremental cost per QALY gained. Study characteristics, economic model, costs, and outcomes were extracted. INBs were calculated in 2022 US dollars adjusted for purchasing power parity. A pooled analysis was performed using a random-effect model stratified by country income level. Heterogeneity was assessed using the Q test and I2 statistic. Key findings and limitations: Thirteen studies with 17 comparisons, ten from high-income (HICs) and three from middle-income (MICs) countries, were included. Ten and five studies compared RARP with ORP and LRP, respectively. From a payer perspective, RARP was cost effective but not statistically significant compared with LRP in HICs (pooled INB: $7507.83 [-$1193.03 to $16 208.69], I2 = 81.15%) and not cost effective in MICs (%; -$4499.39 [-$16 500 to $7526.87], I2 = 17.15%). RARP showed no statistically significant cost effectiveness over ORP in both HICs ($3322.38 [-$1864.39 to $8509.15], I2 = 90.89%) and MICs ($2222.60 [-$2960.64 to $7405.83], I2 = 58.92%). Conclusions and clinical implications: RARP is cost effective compared with LRP in HICs but lacks statistical significance. When compared with ORP, RARP is not cost effective in HICs and MICs. Our findings may support decision-making for prostate cancer treatment options in countries with different health care systems, especially those with limited resources. Patient summary: Our systematic review and meta-analysis provide important information regarding robotic-assisted radical prostatectomy (RARP) compared with open (ORP) and laparoscopic (LRP) radical prostatectomy. In high-income countries, RARP is generally cost effective compared with LRP, but not with ORP, while in middle-income countries, RARP is not cost effective compared with LRP or ORP. The findings of this review can support decision-making for prostate cancer treatment options.