PLOS digital health最新文献

{"title":"Effectiveness of telerehabilitation for adults with neurological conditions in low and middle income countries: A systematic review.","authors":"Eric Nkansah Opoku, Lorna Paul, Derrick Antwi, Katie Thomson, Shadrack Osei Asibey, Marian C Brady, Frederike van Wijck, Fred Stephen Sarfo","doi":"10.1371/journal.pdig.0000911","DOIUrl":"10.1371/journal.pdig.0000911","url":null,"abstract":"<p><p>Neurological conditions including stroke, multiple sclerosis, and Parkinson's significantly contribute to disability and mortality globally. A significant proportion of these cases are found in Low and Middle Income Countries (LMICs). Telerehabilitation has emerged as a promising approach to overcome the geographical, financial, and logistical barriers to rehabilitation in LMICs. However, to date, no review has assessed the effectiveness of telerehabilitation in LMICs. This systematic review aims to evaluate the effectiveness of telerehabilitation for adults with neurological conditions in LMICs. A systematic search of databases (PubMed, EMBASE, CINAHL, MEDLINE, Cochrane central registry for clinical trials, and the World Health Organization (WHO) Global Health Library) was conducted to identify relevant studies published from 1st January 1990-20th April 2024. To accurately capture relevant studies, search terms were closely aligned with PICO elements of the review question. PRISMA and AMSTAR guidelines were used to guide the conduct of this review which only included clinical trials. Joanna Briggs Institute (JBI) critical appraisal tools were used to assess the methodological quality of included studies.Out of 430 identified studies, 16 met the inclusion criteria. There was notable heterogeneity in telerehabilitation content, approaches, dose, delivery methods and follow-up periods. Given the heterogeneity, a narrative analysis was conducted. Findings from the included studies suggest that telerehabilitation can lead to similar or superior outcomes compared to conventional rehabilitation. However, only a third of the included studies incorporated follow-up assessments, and among those, sustained benefits were observed in only a few outcomes. The lack of long-term follow-up data makes it difficult to draw conclusions about the sustained effectiveness of telerehabilitation.This systematic review indicates a promising potential for telerehabilitation to enhance outcomes for adults with neurological conditions living in LMICs. However, the lack of long-term follow-up data limits understanding of sustained benefits. High-quality, methodologically rigorous research with extended follow-up is needed to determine the long-term effectiveness of telerehabilitation. Establishing this evidence base is critical for integrating telerehabilitation into healthcare strategies and policy.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000911"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12233225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for the prediction of urosepsis using electronic health record data.","authors":"Varuni Sarwal, Nadav Rakocz, Georgina Dominique, Jeffrey N Chiang, A Lenore Ackerman","doi":"10.1371/journal.pdig.0000896","DOIUrl":"10.1371/journal.pdig.0000896","url":null,"abstract":"<p><p>Urosepsis, a medical condition resulting from the progression of urinary tract infection (UTI), is a leading cause of death in the US. Urosepsis occurs due to complicated UTI and constitutes ~25% of sepsis cases. Early prediction of urosepsis is critical in providing personalized care, reducing diagnostic uncertainty, lowering mortality rates. While machine learning (ML) techniques have the potential to aid healthcare professionals in identifying risk factors and recommending treatment options, no study has been developed to predict the development of urosepsis in patients with a suspected UTI in outpatient settings. We develop and evaluate ML models in predicting hospital admission and urosepsis diagnosis for patients with an outpatient UTI encounter, leveraging de-identified electronic health records sourced from UCLA. Inclusion criteria included a positive diagnosis of urinary tract infection indicated by ICD-10 code N30/N93.0 and positive bacteria result via urinalysis in an ambulatory setting. W extracted demographic information, urinalysis findings, and antibiotics prescribed for each instance of UTI. Reencounters we defined as encounters within seven days of the initial UTI. Reencounters were considered urosepsis-related if matching positive blood and urine cultures were found with a sepsis ICD-10 code of A41/R78/R65. ML models were trained and evaluated on two tasks: prediction of a reencounter leading to hospitalization, prediction of Urosepsis. Model performances were stratified by ethnicities. Random forest models achieved significant improvement over baseline performance (APR = 0.004), with APR = 0.15 for reencounters and 0.31 for urosepsis prediction. While these APR values reflect the challenge of predicting rare events (0.4% prevalence), they represent meaningful predictive power for clinical risk stratification. We computed Shapley values to interpret model predictions and found patient age, sex, and urinary WBC-count were the top three predictive features. Our study has the potential to assist clinicians in the identification of high-risk patients, informed decisions about antibiotic prescription and improving patient care.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000896"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12225808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What does it take to design digitally enabled performance management and incentive interventions for community health programs: Lessons from Ethiopia.","authors":"Alemnesh Hailemariam Mirkuzie, Yared Kifle, Gizachew Tadele Tiruneh, Girma Tadesse, Getnet Alem Teklu, Esubalew Sebsibe, Eyoel Mitiku, Aklilu Abera, Wondwossen Shiferaw, Birhutesfa Bekele, Wuleta Aklilu Betemariam, Desalew Emaway","doi":"10.1371/journal.pdig.0000914","DOIUrl":"10.1371/journal.pdig.0000914","url":null,"abstract":"<p><p>The Health Extension Program (HEP) in Ethiopia has faced multifaceted challenges, including declining motivation and suboptimal performance of Health Extension Workers (HEWs). These issues have significantly impacted Reproductive, Maternal, Newborn, and Child Health (RMNCH) outcomes. To address these gaps, JSI, in collaboration with key partners, designed digitally enabled performance management (PM) and performance-based incentive (PBI) interventions integrated into the electronic Community Health Information System (eCHIS). A multi-approach design process was implemented, including a landscape review of existing strategies, human-centered design (HCD), and participatory co-design workshops. National and regional stakeholders contributed to the development process to ensure contextual relevance. A hybrid framework combining Management by Objectives (MBO) and the DESC (Digitally enabled, Equipped, Supervised, Compensated) model guided the design. The digitally enabled PM/PBI interventions required significant advancement to the eCHIS application suite, such as enhancing the existing focal person application (FPA) with real-time monitoring dashboards, digital target setting, and automated supervision features, and developing a national eCHIS dashboard for supervisory support, data-informed performance evaluation, and decision making. Twenty-two key performance indicators (KPIs) were identified to measure outputs, health outcomes, and supervisory processes. The intervention integrated digitally supported supervision and mentorship to drive performance improvements. Stakeholders proposed incentivizing the HEWs, supervisors, and HPs who record high performance biannually as a team and/or an individual with non-financial or mixed incentives. In conclusion, the participatory design process resulted in robust, scalable PM/PBI interventions tailored to Ethiopia's HEP. Digitally enabled tools, when aligned with supportive supervision and sustainable incentive strategies, have the potential to improve HEW motivation, RMNCH outcomes, and health system accountability. This model offers valuable lessons for other low-resource settings implementing performance management systems in community health programs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000914"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12225878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives of family medicine residents on artificial intelligence for survival estimation in patients with serious illness.","authors":"Gemma Postill, Anglin Dent, Jill Dombroski, Amol A Verma, Jeff Myers, Tavis Apramian","doi":"10.1371/journal.pdig.0000917","DOIUrl":"10.1371/journal.pdig.0000917","url":null,"abstract":"<p><p>As technology for artificial intelligence (AI) in medicine has rapidly proliferated, research is needed on how AI should be used in healthcare. Family physicians could deploy AI to predict survival in serious illness which is a particularly difficult task given the breadth of diseases encountered in primary care. Little research exists to inform whether survival estimation tools are welcome in primary care to manage serious illness prognostication. To address this gap, we elicited the perspectives of family medicine residents on the potential use of AI to help them predict survival (i.e., time expected) for their patients with serious illness. Our qualitative study draws on semi-structured interview data from 18 family medicine residents in Canada. We used a pragmatic framework to conduct our analysis, employing principles of constructivist grounded theory. We identified that family medicine residents were receptive to AI survival estimation for serious illness management, particularly for supporting their delivery of expert advice over a broad range of clinical topics. However, caring for patients with serious illness in primary care involves more than survival estimation, with such a tool having likely only limited applicability to end of life. Summarizing these perspectives, we identified four themes: (1) improving patient care with AI, (2) AI with a grain of salt, (3) patient-driven use of AI, and (4) augmenting, not replacing family physicians. Thus, survival estimation with AI for serious illness has potential clinical value in primary care. In addition to survival, pertinent challenges to address with AI include understanding of expected function, maximizing quality of life, and response to interventions, in addition to quantifying survival time. Future prognostication models should consider use of additional patient-centered outcomes and modifying the outcomes predicted based on prediction timepoints. To successfully deploy these technologies in primary care, additional education and role modelling of technology use is needed.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000917"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and feasibility of smartphone-based digital phenotyping for long-term mental health monitoring in adolescents.","authors":"Debbie Huang, Patrick Emedom-Nnamdi, Jukka-Pekka Onnela, Anna Van Meter","doi":"10.1371/journal.pdig.0000883","DOIUrl":"10.1371/journal.pdig.0000883","url":null,"abstract":"<p><p>Assessment of psychiatric symptoms relies on subjective self-report, which can be unreliable. Digital phenotyping collects data from smartphones to provide near-continuous behavioral monitoring. It can be used to provide objective information about an individual's mental state to improve clinical decision-making for both diagnosis and prognostication. The goal of this study was to evaluate the feasibility and acceptability of smartphone-based digital phenotyping for long-term mental health monitoring in adolescents with bipolar disorder and typically developing peers. Participants (aged 14-19) with bipolar disorder (BD) or with no mental health diagnoses were recruited for an 18-month observational study. Participants installed the Beiwe digital phenotyping app on their phones to collect passive data from their smartphone sensors and thrice-weekly surveys. Participants and caregivers were interviewed monthly to assess changes in the participant's mental health. Analyses focused on 48 participants who had completed participation. Average age at baseline was 15.85 years old (SD = 1.37). Approximately half (54%) identified as female, and 54% identified with a minoritized racial/ethnic background. Completion rates across data types were high, with 99% (826/835) of clinical interviews completed, 89% of passive data collected (22,233/25,029), and 47% (4,945/10,448) of thrice-weekly surveys submitted. The proportion of days passive data were collected was consistent over time for both groups; the clinical interview and active survey completion decreased over the study course. Results of this study suggest digital phenotyping has significant potential as a method of long-term mental health monitoring in adolescents. In contrast to traditional methods, including interview and self-report, it is lower burden and provides more complete data over time. A necessary next step is to determine how well the digital data capture changes in mental health to determine the clinical utility of this approach.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 7","pages":"e0000883"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Racial disparities in continuous glucose monitoring-based 60-min glucose predictions among people with type 1 diabetes.","authors":"Helene Bei Thomsen, Livie Yumeng Li, Anders Aasted Isaksen, Benjamin Lebiecka-Johansen, Charline Bour, Guy Fagherazzi, William P T M van Doorn, Tibor V Varga, Adam Hulman","doi":"10.1371/journal.pdig.0000918","DOIUrl":"10.1371/journal.pdig.0000918","url":null,"abstract":"<p><p>Non-Hispanic white (White) populations are overrepresented in medical studies. Potential healthcare disparities can happen when machine learning models, used in diabetes technologies, are trained on data from primarily White patients. We aimed to evaluate algorithmic fairness in glucose predictions. This study utilized continuous glucose monitoring (CGM) data from 101 White and 104 Black participants with type 1 diabetes collected by the JAEB Center for Health Research, US. Long short-term memory (LSTM) deep learning models were trained on 11 datasets of different proportions of White and Black participants and tailored to each individual using transfer learning to predict glucose 60 minutes ahead based on 60-minute windows. Root mean squared errors (RMSE) were calculated for each participant. Linear mixed-effect models were used to investigate the association between racial composition and RMSE while accounting for age, sex, and training data size. A median of 9 weeks (IQR: 7, 10) of CGM data was available per participant. The divergence in performance (RMSE slope by proportion) was not statistically significant for either group. However, the slope difference (from 0% White and 100% Black to 100% White and 0% Black) between groups was statistically significant (p = 0.02), meaning the RMSE increased 0.04 [0.01, 0.08] mmol/L more for Black participants compared to White participants when the proportion of White participants increased from 0 to 100% in the training data. This difference was attenuated in the transfer learned models (RMSE: 0.02 [-0.01, 0.05] mmol/L, p = 0.20). The racial composition of training data created a small statistically significant difference in the performance of the models, which was not present after using transfer learning. This demonstrates the importance of diversity in datasets and the potential value of transfer learning for developing more fair prediction models.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000918"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12208448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation.","authors":"Fu Zheng, Liu XingMing, Xu JuYing, Tao MengYing, Yang BaoJian, Shan Yan, Ye KeWei, Lu ZhiKai, Huang Cheng, Qi KeLan, Chen XiHao, Du WenFei, He Ping, Wang RunYu, Ying Ying, Bu XiaoHui","doi":"10.1371/journal.pdig.0000738","DOIUrl":"10.1371/journal.pdig.0000738","url":null,"abstract":"<p><p>This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. The study used YOLOv8 as the AI model. During the AI model training, in addition to conventional image augmentation techniques, augmentation methods specifically tailored for ultrasound images were employed to balance the quantity differences between thyroid nodule classes and enhance model training effectiveness. The study found that training the model with augmented data significantly outperformed training with raw images data. When the number of original images number was only 1,360, with 7 thyroid nodule classifications, pre-annotation using the AI model trained on augmented data could save at least 30% of the manual annotation workload for junior physicians. When the scale of original images number reached 6,800, the classification accuracy of the AI model trained on augmented data was very close with that of junior physicians, eliminating the need for manual preliminary annotation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000738"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12208487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing artificial intelligence tools for institutional review board pre-review: A pilot study on ChatGPT's accuracy and reproducibility.","authors":"Yasuko Fukataki, Wakako Hayashi, Naoki Nishimoto, Yoichi M Ito","doi":"10.1371/journal.pdig.0000695","DOIUrl":"10.1371/journal.pdig.0000695","url":null,"abstract":"<p><p>This pilot study is the first phase of a broader project aimed at developing an explainable artificial intelligence (AI) tool to support the ethical evaluation of Japanese-language clinical research documents. The tool is explicitly not intended to assist document drafting. We assessed the baseline performance of generative AI-Generative Pre-trained Transformer (GPT)-4 and GPT-4o-in analyzing clinical research protocols and informed consent forms (ICFs). The goal was to determine whether these models could accurately and consistently extract ethically relevant information, including the research objectives and background, research design, and participant-related risks and benefits. First, we compared the performance of GPT-4 and GPT-4o using custom agents developed via OpenAI's Custom GPT functionality (hereafter \"GPTs\"). Then, using GPT-4o alone, we compared outputs generated by GPTs optimized with customized Japanese prompts to those generated by standard prompts. GPT-4o achieved 80% agreement in extracting research objectives and background and 100% in extracting research design, while both models demonstrated high reproducibility across ten trials. GPTs with customized prompts produced more accurate and consistent outputs than standard prompts. This study suggests the potential utility of generative AI in pre-institutional review board (IRB) review tasks; it also provides foundational data for future validation and standardization efforts involving retrieval-augmented generation and fine-tuning. Importantly, this tool is intended not to automate ethical review but rather to support IRB decision-making. Limitations include the absence of gold standard reference data, reliance on a single evaluator, lack of convergence and inter-rater reliability analysis, and the inability of AI to substitute for in-person elements such as site visits.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000695"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12208443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of photoplethysmography signal quality at the wrist.","authors":"Peter H Charlton, Vaidotas Marozas, Elisa Mejía-Mejía, Panicos A Kyriacou, Jonathan Mant","doi":"10.1371/journal.pdig.0000585","DOIUrl":"10.1371/journal.pdig.0000585","url":null,"abstract":"<p><p>Wrist photoplethysmogram (PPG) signals are widely used for physiological monitoring in consumer devices. However, the PPG is highly susceptible to noise, which can reduce the accuracy of monitored parameters. The aim of this study was to identify factors which influence PPG signal quality. Data from the Aurora-BP dataset were used, consisting of reflectance wrist PPG signals measured from 1,142 subjects of varying ages and health statuses. Measurements were acquired in supine, sitting, and standing postures, and with the sensor held at different heights. Three signal quality metrics were calculated: the signal-to-noise ratio (SNR), the perfusion index (PI), and the template-matching correlation coefficient (TMCC). When comparing between postures with the sensor held at a natural height, quality was greatest in the supine position (SNR: 18.6 dB), followed by sitting with the arm resting in the lap (13.7 dB), and lowest whilst standing with the arm hanging alongside (9.0 dB) (p < 0.001). Signal quality increased as the arm was raised to heart height: whilst sitting, quality was lowest with the arm alongside the body (10.5 dB), and increased when the sensor was held in the lap (13.7 dB) and at heart height (15.5 dB) (p < 0.001). Similar trends were observed for the TMCC and PI. Findings were mixed for the influence of participant characteristics on signal quality. The SNR and TMCC, but not the PI, increased with age. The SNR either decreased or remained constant at darker skin tones when controlling for PPG DC amplitude, compared to constant or increased when allowing DC amplitude to vary. In conclusion, this study identified the impacts of posture and sensor height on signal quality, with highest qualities observed in the supine posture and with the sensor at heart height. It also highlights the importance of adjusting LED light intensity to maintain signal quality across skin tones.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000585"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144512909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nursing education research in Sub-Saharan Africa: A systematic review and bibliometric analysis.","authors":"Beth Waweru, Peter Gatiti, Serah Wachira","doi":"10.1371/journal.pdig.0000900","DOIUrl":"10.1371/journal.pdig.0000900","url":null,"abstract":"<p><p>Nursing education is pivotal for ensuring competent healthcare professionals, and its improvement is essential for enhancing the quality of health care systems globally. This study focuses on nursing education research in Sub-Saharan Africa (SSA) over the last decade, employing both bibliometric analysis and systematic review methodologies. The bibliometric analysis reveals an evolving landscape of nursing education research in SSA, offering insights into trends, key countries, journals, and predominant research themes. Notably, the study identifies a scarcity of literature using bibliometric approaches in nursing research, addressing this gap by providing a comprehensive overview of the field.The systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, explores 1359 articles published in the last ten years, focusing on nursing education in SSA. The analysis of 1288 selected articles emphasize experiences and challenges faced by nursing and midwifery students during their education and clinical training. The emerging themes cuts across classroom teaching, clinical learning environments, and overall clinical practice. The findings highlight the need for attention to educational support, effective communication, professionalism, inclusivity, and innovative teaching methods. Limitations include the exclusive focus on SSA, restricting generalizability to other regions. Nonetheless, the study offers valuable insights for educators, policymakers, and institutions to enhance the quality of nursing education. By addressing identified challenges, fostering innovation, and promoting inclusivity, stakeholders can better prepare students to meet the dynamic demands of the healthcare profession in SSA and potentially other regions, especially Low- and Middle-income Countries. The research contributes to the ongoing efforts to bridge the gap between nursing education theory and practice, ultimately improving healthcare outcomes in the region.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000900"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12200722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}