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Orphan, clock keeper, and thief, twelve-year-old Hugo lives in the walls of a busy Paris train station, where his survival depends on secrets and anonymity. But when his world suddenly interlocks with an eccentric girl and the owner of a small toy booth in the train station, Hugo's undercover life, and his most precious secret, are put in jeopardy. A cryptic drawing, a treasured notebook, a stolen key, a mechanical man, and a hidden message all come together to change Hugo's life forever.

The mixed methods appraisal tool (MMAT) was developed for critically appraising different study designs. This study aimed to improve the content validity of three of the five categories of studies in the MMAT by identifying relevant methodological criteria for appraising the quality of qualitative, survey, and mixed methods studies. First, we performed a literature review to identify critical appraisal tools and extract methodological criteria. Second, we conducted a two-round modified e-Delphi technique. We asked three method-specific panels of experts to rate the relevance of each criterion on a five-point Likert scale. A total of 383 criteria were extracted from 18 critical appraisal tools and a literature review on the quality of mixed methods studies, and 60 were retained. In the first and second rounds of the e-Delphi, 73 and 56 experts participated, respectively. Consensus was reached for six qualitative criteria, eight survey criteria, and seven mixed methods criteria. These results led to modifications of eight of the 11 MMAT (version 2011) criteria. Specifically, we reformulated two criteria, replaced four, and removed two. Moreover, we added six new criteria. Results of this study led to improve the content validity of this tool, revise it, and propose a new version (MMAT version 2018).

Patient experience data from social media offer patient-centered perspectives on disease, treatments, and health service delivery. Current guidelines typically rely on systematic reviews, while qualitative health studies are often seen as anecdotal and nongeneralizable. This study explores combining personal health experiences from multiple sources to create generalizable evidence. The study aims to (1) investigate how combining unsupervised natural language processing (NLP) and corpus linguistics can explore patient perspectives from a large unstructured dataset of modafinil experiences, (2) compare findings with Cochrane meta-analyses on modafinil's effectiveness, and (3) develop a methodology for analyzing such data. Using 69,022 posts from 790 sources, we used a variety of NLP and corpus techniques to analyze the data, including data cleaning techniques to maximize post context, Python for NLP techniques, and Sketch Engine for linguistic analysis. We used multiple topic mining approaches, such as latent Dirichlet allocation, nonnegative matrix factorization, and word-embedding methods. Sentiment analysis used TextBlob and Valence Aware Dictionary and Sentiment Reasoner, while corpus methods including collocation, concordance, and n-gram generation. Previous work had mapped topic mining to themes, such as health conditions, reasons for taking modafinil, symptom impacts, dosage, side effects, effectiveness, and treatment comparisons. Key findings of the study included modafinil use across 166 health conditions, most frequently narcolepsy, multiple sclerosis, attention-deficit disorder, anxiety, sleep apnea, depression, bipolar disorder, chronic fatigue syndrome, fibromyalgia, and chronic disease. Word-embedding topic modeling mapped 70% of posts to predefined themes, while sentiment analysis revealed 65% positive responses, 6% neutral responses, and 28% negative responses. Notably, the perceived effectiveness of modafinil for various conditions strongly contrasts with the findings of existing randomized controlled trials and systematic reviews, which conclude insufficient or low-quality evidence of effectiveness. This study demonstrated the value of combining NLP with linguistic techniques for analyzing large unstructured text datasets. Despite varying opinions, findings were methodologically consistent and challenged existing clinical evidence. This suggests that patient-generated data could potentially provide valuable insights into treatment outcomes, potentially improving clinical understanding and patient care.

In-patients in crisis report poor experiences of mental healthcare not conducive to recovery. Concerns include coercion by staff, fear of assault from other patients, lack of therapeutic opportunities and limited support. There is little high-quality evidence on what is important to patients to inform recovery-focused care.AimsTo conduct a systematic review of published literature, identifying key themes for improving experiences of in-patient mental healthcare. A systematic search of online databases (MEDLINE, PsycINFO and CINAHL) for primary research published between January 2000 and January 2016. All study designs from all countries were eligible. A qualitative analysis was undertaken and study quality was appraised. A patient and public reference group contributed to the review. Studies (72) from 16 countries found four dimensions were consistently related to significantly influencing in-patients' experiences of crisis and recovery-focused care: the importance of high-quality relationships; averting negative experiences of coercion; a healthy, safe and enabling physical and social environment; and authentic experiences of patient-centred care. Critical elements for patients were trust, respect, safe wards, information and explanation about clinical decisions, therapeutic activities, and family inclusion in care. A number of experiences hinder recovery-focused care and must be addressed with the involvement of staff to provide high-quality in-patient services. Future evaluations of service quality and development of practice guidance should embed these four dimensions.Declaration of interestK.B. is editor of British Journal of Psychiatry and leads a national programme (Synergi Collaborative Centre) on patient experiences driving change in services and inequalities.

Purpose Social media has led to fundamental changes in the way that people look for and share health related information. There is increasing interest in using this spontaneously generated patient experience data as a data source for health research. The aim was to summarise the state of the art regarding how and why SGOPE data has been used in health research. We determined the sites and platforms used as data sources, the purposes of the studies, the tools and methods being used, and any identified research gaps. Methods A scoping umbrella review was conducted looking at review papers from 2015 to Jan 2021 that studied the use of SGOPE data for health research. Using keyword searches we identified 1759 papers from which we included 58 relevant studies in our review. Results Data was used from many individual general or health specific platforms, although Twitter was the most widely used data source. The most frequent purposes were surveillance based, tracking infectious disease, adverse event identification and mental health triaging. Despite the developments in machine learning the reviews included lots of small qualitative studies. Most NLP used supervised methods for sentiment analysis and classification. Very early days, methods need development. Methods not being explained. Disciplinary differences - accuracy tweaks vs application. There is little evidence of any work that either compares the results in both methods on the same data set or brings the ideas together. Conclusion Tools, methods, and techniques are still at an early stage of development, but strong consensus exists that this data source will become very important to patient centred health research.

ObjectivesTo systematically review interventions that include an element of menstrual education delivered to young adolescent girls.DesignThis was a systematic review and meta-analysis. Selected articles were quality assessed using the Mixed Methods Appraisal Tool quality appraisal checklist. A meta-analysis was conducted on a subset of articles, and the effect size of the intervention was calculated using Cohen’s d. A logic model was constructed to frame the effect of menstrual education interventions on menstrual health.SettingPapers reporting on interventions in high-income and low-income and middle-income countries were sought.Information sourcesSeven electronic databases were searched for English-language entries that were published between January 2014 and May 2020.ParticipantsThe interventions were aimed at younger adolescent girls aged 10–14 years old.InterventionsThe interventions were designed to improve the menstrual health of the recipients, by addressing one or more elements of menstrual knowledge, attitude or practices (KAP).Eligibility criteriaInterventions that had not been evaluated were excluded.Primary and secondary outcomesThe most common type of output was a difference in knowledge or skill score ascertained from a pre and post test. Some studies measured additional outcomes, such as attitude or confidence.ResultsTwenty-four eligible studies were identified. The number of participants varied from 1 to 2564. All studies reported improvements in menstrual KAP. The meta-analysis indicates that larger effect sizes were attained by those that encouraged discussion than those that distributed pamphlets.ConclusionsEducation interventions are effective in increasing the menstrual knowledge of young adolescent girls and skills training improves competency to manage menstruation more hygienically and comfortably. Interactive interventions are more motivating than didactic or written. Sharing concerns gives girls confidence and helps them to gain agency on the path to menstrual health.Trial registration numberFor this review, a protocol was not prepared or registered.

There has been a substantial increase in the development of artificial intelligence (AI) tools for clinical decision support. Historically, these were mostly knowledge-based systems, but recent advances include non-knowledge-based systems using some form of machine learning. The ability of health care professionals to trust technology and understand how it benefits patients or improves care delivery is known to be important for their adoption of that technology. For non-knowledge-based AI tools for clinical decision support, these issues are poorly understood. The aim of this study is to qualitatively synthesize evidence on the experiences of health care professionals in routinely using non-knowledge-based AI tools to support their clinical decision-making. In June 2023, we searched 4 electronic databases, MEDLINE, Embase, CINAHL, and Web of Science, with no language or date limit. We also contacted relevant experts and searched reference lists of the included studies. We included studies of any design that reported the experiences of health care professionals using non-knowledge-based systems for clinical decision support in their work settings. We completed double independent quality assessment for all included studies using the Mixed Methods Appraisal Tool. We used a theoretically informed thematic approach to synthesize the findings. After screening 7552 titles and 182 full-text articles, we included 25 studies conducted in 9 different countries. Most of the included studies were qualitative (n=13), and the remaining were quantitative (n=9) and mixed methods (n=3). Overall, we identified 7 themes: health care professionals' understanding of AI applications, level of trust and confidence in AI tools, judging the value added by AI, data availability and limitations of AI, time and competing priorities, concern about governance, and collaboration to facilitate the implementation and use of AI. The most frequently occurring are the first 3 themes. For example, many studies reported that health care professionals were concerned about not understanding the AI outputs or the rationale behind them. There were issues with confidence in the accuracy of the AI applications and their recommendations. Some health care professionals believed that AI provided added value and improved decision-making, and some reported that it only served as a confirmation of their clinical judgment, while others did not find it useful at all. Our review identified several important issues documented in various studies on health care professionals' use of AI tools in real-world health care settings. Opinions of health care professionals regarding the added value of AI tools for supporting clinical decision-making varied widely, and many professionals had concerns about their understanding of and trust in this technology. The findings of this review emphasize the need for concerted efforts to optimize the integration of AI tools in real-world health care settings. PROSPERO CRD42022336359; https://tinyurl.com/2yunvkmb.

IntroductionAs more countries are moving towards universal health care, middle-income countries in particular are trying to expand coverage, often using public funds. Electronic health records (EHR) are useful in monitoring patient outcomes, the performance of providers, and so the use of those public funds. With the multiple institutions or departments responsible for providing care to any individual, rather than a single record, an EHR is the interface through which to view data from a digital health information eco-system that draws on data from many different sources. South Africa plans to establish a National Health Insurance fund where EHRs will be essential for monitoring outcomes, and informing purchasing decisions. Despite various relevant policies and South Africa's relative wealth and digital capability, progress has been slow. In this paper, we explore the barriers and facilitators to implementing electronic health records in South Africa.MethodsIn this qualitative study, we conducted in-depth interviews with participants including academics, staff at parastatals, managers in the private health sector, NGO managers and government staff at various levels.ResultsThe Western Cape provincial government over a 20-year period has managed to develop a digital health information ecosystem by drawing together existing data systems and building new systems. However, despite having the necessary policies in place and a number of stand-alone population level digital health information systems, several barriers still stand in the way of building national electronic health records and an efficient digital health ecosystem. These include a lack of national leadership and conflict, a failure to understand the scope of the task required to achieve scale up, insufficient numbers of technically skilled staff, failure to use the tender system to generate positive outcomes, and insufficient investment towards infrastructural needs such as hardware, software and connectivity.ConclusionFor South Africa to have an effective electronic health record, it is important to start by overcoming the barriers to interoperability, and to develop the necessary underlying digital health ecosystem. Like the Western Cape, provincial governments need to integrate and build on existing systems as their next steps forward.

IntroductionCommunity health workers (CHWs) enable marginalised communities, often experiencing structural poverty, to access healthcare. Trust, important in all patient–provider relationships, is difficult to build in such communities, particularly when stigma associated with HIV/AIDS, tuberculosis and now COVID-19, is widespread. CHWs, responsible for bringing people back into care, must repair trust. In South Africa, where a national CHW programme is being rolled out, marginalised communities have high levels of unemployment, domestic violence and injury.ObjectivesIn this complex social environment, we explored CHW workplace trust, interpersonal trust between the patient and CHW, and the institutional trust patients place in the health system.Design, participants, settingWithin the observation phase of a 3-year intervention study, we conducted interviews, focus groups and observations with patients, CHWs, their supervisors and, facility managers in Sedibeng.ResultsCHWs had low levels of workplace trust. They had recently been on strike demanding better pay, employment conditions and recognition of their work. They did not have the equipment to perform their work safely, and some colleagues did not trust, or value, their contribution. There was considerable interpersonal trust between CHWs and patients, however, CHWs’ efforts were hampered by structural poverty, alcohol abuse and no identification documents among long-term migrants. Those supervisors who understood the extent of the poverty supported CHW efforts to help the community. When patients had withdrawn from care, often due to nurses’ insensitive behaviour, the CHWs’ attempts to repair patients’ institutional trust often failed due to the vulnerabilities of the community, and lack of support from the health system.ConclusionStrategies are needed to build workplace trust including supportive supervision for CHWs and better working conditions, and to build interpersonal and institutional trust by ensuring sensitivity to social inequalities and the effects of structural poverty among healthcare providers. Societies need to care for everyone.

Background Many low and middle- income countries (LMICs) are repositioning community health worker (CHW) programmes to provide a more comprehensive range of promotive and preventive services and referrals to the formal health service. However, insufficient supervision, fragmented programmes, and the low literacy levels of CHWs often result in the under-performance of the programmes. We evaluate the impact of a roving nurse mentor working with CHW teams proving supportive supervision in a semi-rural area of South Africa. Methods We conducted a longitudinal process evaluation, using in-depth interviews, focus groups and observations prior to the intervention, during the intervention, and 6 months post-intervention to assess how the effects of the intervention were generated and sustained. Our participants were CHWs, their supervisors, clients and facility staff members and community representatives. Results The nurse mentor operated in an environment of resource shortages, conflicts between CHWs and facility staff, and an active CHW labour union. Over 15 months, the mentor was able to (1) support and train CHWs and their supervisors to gain and practice new skills, (2) address their fears of failing and (3) establish operational systems to address inefficiencies in the CHWs’ activities, resulting in improved service provision. Towards the end of the intervention the direct employment of the CHWs by the Department of Health and an increase in their stipend added to their motivation and integration into the local primary care clinic team. However, given the communities’ focus on accessing government housing, rather than better healthcare, and volatile nature of the communities, the nurse mentor was not able to establish a collaboration with local structures. Conclusions A roving nurse mentor overseeing several CHW teams within a district healthcare system is a feasible option, particularly in a context where there is a shortage of qualified supervisors to support CHWs activities. A roving nurse mentor can contribute to the knowledge and skills development of the CHWs and enhance the capacity of junior supervisors. However, the long-term sustainability of the effects of intervention is dependent on CHWs’ formal employment by the Department of Health.