BMC Medical Education

Introduction: Trans and gender-diverse (TGD) individuals often face stigma and discrimination in healthcare, hindering access to gender-affirming care. Training healthcare workers on TGD health aims to foster inclusive and affirming care practices. This review aimed to evaluate the effectiveness of TGD health training programs for healthcare workers. Methods: This systematic review followed the PRISMA guidelines and was registered with PROSPERO (CRD42023443288). We searched 13 databases for studies up to March 2024, with no language/geographic restrictions. Ten reviewers screened studies in pairs, resolving discrepancies via discussion or third-reviewer input. We included randomized/non-randomized comparative and before-after studies for quantitative analysis (mean difference [MD] or standardized mean difference [SMD] with 95% CIs) and qualitative/mixed-methods studies for thematic synthesis. Evidence certainty was assessed using GRADE (quantitative) and GRADE-CERQual (qualitative). Outcomes included knowledge, attitudes, skills, discrimination, competence, comfort, TGD quality of life, and stakeholder preferences. Results: From 20,188 records, 85 studies were included. Training appears to have improved healthcare workers' knowledge (SMD=1.08, 95% CI 0.78-1.39), attitudes (SMD=0.22, 95% CI 0.05-0.39), skills (SMD=0.96, 95% CI 0.56-1.37), competence (SMD=0.55, 95% CI 0.29-0.81), and comfort (SMD=0.69, 95% CI 0.17-1.21). Qualitative analysis of 130 findings identified 18 categories and four key themes on intervention design and impact. Conclusions: TGD training programs may enhance health workers' knowledge, attitudes, skills, competence, and comfort. Well-structured, interactive, and inclusive programs showed promise, but evidence certainty was low with limited follow-up. Further high-quality research is needed to confirm these findings.

Background Sierra Leones neonatal mortality rate is among the highest in the world. Koidu Government Hospital opened a Special Care Baby Unit (SCBU) in 2020. To increase knowledge of the SCBU health care providers (HCPs), a neonatal curriculum was implemented to facilitate HCP education on management of neonatal conditions. The aim of this study was to understand the effect of the curriculum on knowledge acquisition and the perception of the teaching methodologies among participating HCPs. Methods US-based mentors facilitated a two-phase, flipped classroom, virtual neonatal medicine curriculum between October 2024 and April 2025, followed by one-week in-person education sessions with SCBU HCPs. With each phase, participants completed pre- and post-test educational assessments. At the end of the curriculum, they completed a subjective assessment to capture perceptions related to the quality of teaching methodologies integrated within the curriculum. Wilcoxon signed rank test was used to assess pre- versus post-test change. Descriptive statistics were used to analyse the subjective assessment. Results Thirty-eight participants completed the educational assessments, 30 (79%) took all four pre- and post-tests; 25/38 (65.8%) were female, 27 (71.1%) were nurses. Median correct answers for both phases increased from the pre- to post-test for individual learners [Phase 1, pre-test 14/27 (51.9%), post-test 23/27 (85.2%), p<0.001], [Phase 2, pre-test 14/25 (56.0%), post-test 23/25 (92.0%), p <0.001]. Thirty-one participants completed the subjective assessment, of whom 96.8% (30/31) rated the curriculum to be "very effective." All 31 participants indicated that the in-person instruction was "very helpful." Through open text responses, they offered valuable insight into challenges, strengths, and next steps. Conclusion This neonatal curriculum resulted in significantly increased knowledge and was well regarded. Adapting this curriculum or similar curricula show promise to improve the quality of care for small and/or sick neonates in low resource settings.

Background: Qualitative studies have noted that the burden of family planning disproportionately falls on women in India. Our primary objective was to quantify the gender disparity in the uptake of surgical sterilizations. Our secondary objectives were to calculate the costs of tubectomies and vasectomies in India and to estimate the savings of scaling up vasectomy rates. Methods: We conducted a retrospective analysis using data on the total number of tubectomies and vasectomies performed, postoperative failure, and postoperative mortality due to these procedures, obtained from the Health Management Information System (HMIS) for 2019-20. We calculated the vasectomy (tubectomy) operative rates per 10,000 men (women) of reproductive age (15-49 years). The women-to-men ratio of these rates is used as a proxy for sex-based disparities in uptake. State-specific procedure costs and compensation for failures and postoperative deaths at public hospitals were extracted and aggregated from government data and research studies. To estimate the financial benefit of scaling up vasectomies, the cost of increasing the vasectomy rate to 50% of the total sterilization rate was calculated. All costs were adjusted for inflation to 2022 and presented in United States Dollars (USD). Findings: In 2019-20, the national tubectomy rate was 96.5, the vasectomy rate was 1.4, and the resulting women-to-men rate ratio was 67.5. The cost per tubectomy procedure was 3.5 times that of vasectomy (89.1 USD vs. 25.3 USD). Keeping the overall operative rate constant, the net savings from scaling up vasectomies to at least 50% of total operations (replacing excess tubectomies) range from 62,193,487 to 75,355,777 USD. Interpretation: Our pan-India analysis confirms that the use of surgical family planning methods is disproportionately higher among women. Scaling up vasectomies has finacial benefits and can improve gender equity. Funding: None.

This study aims to assess women's perceptions of artificial intelligence (AI) used in breast cancer screening in France by examining their knowledge of AI and the barriers to their participation in organized screening. The results of a survey conducted in June 2025 among a national sample of 2000 women (aged 40-75) reveal limited participation and persistent concerns among women. Nevertheless, despite a low awareness of specific AI applications, a large majority of the women surveyed are very favorable to the use of AI in breast cancer diagnosis, even considering it a lever to increase screening participation.

Machine learning (ML) has emerged as a transformative technology across biomedical and life science sectors, with applications spanning drug discovery, medical imaging, genomics, and clinical decision support (Goecks et al., 2020; Patel et al., 2020). Despite exponential growth in ML-related publications, from fewer than 100 articles in 2003 to nearly 25,000 by 2021 (NCBI, 2022), adoption among industry professionals remains uneven and sector-dependent. Understanding what drives or inhibits this adoption is critical for organisations seeking to leverage ML capabilities in research and clinical practice. Technology adoption in organisational contexts has been extensively studied through the Technology Acceptance Model (TAM), originally proposed by Davis (1989) and subsequently extended to incorporate external variables influencing perceived usefulness (PU) and perceived ease of use (PEU) (Venkatesh & Davis, 1996). While TAM has been applied across multiple industries, its application within biomedical and life science contexts remains limited, and the industry-specific factors that shape ML acceptance in this sector have not been systematically examined. Two external variables are particularly relevant to life science professionals. First, the bibliometric journal impact factor (JIF) functions as a cognitive signal of scientific credibility, a sector where evidence-based decision-making is culturally embedded, and publication quality serves as a proxy for technological legitimacy (Garfield, 1996). Second, technology hype, operationalised through the Gartner Hype Cycle framework, represents a social influence variable that shapes organisational expectations and investment decisions around emerging technologies (Gartner Inc., 2018). Whether these variables influence ML acceptance among life science professionals, alongside individual knowledge and experience, has not been empirically tested. This study addresses that gap by investigating ML technology acceptance among 213 biomedical and life science professionals across EMEA, LATAM, and North America, using a cross-sectional quantitative survey and PLS-SEM analysis. The TAM model is extended with three external variables, JIF, technology hype, and prior knowledge and experience, to test their influence on PU and PEU in this specific professional context. Additionally, the study examines demographic and regional differences in ML acceptance, with particular attention to variation between academic researchers and healthcare professionals. The findings contribute a validated, sector-specific extension of TAM for life sciences, provide actionable insights for organisations seeking to accelerate ML implementation, and establish a framework for future subsector-specific research.

Introduction: Early hospital presentation after stroke onset is necessary for rapid assessment and access to time-dependent acute management. This study examined the correlates of late presentation for stroke care among patients recorded at a tertiary hospital in Ondo State, Nigeria. Methods: A retrospective records review was conducted using secondary data from the Stroke Registry of the University of Medical Sciences Teaching Hospital, radiology department records, referral notes, and ambulance records. Records of stroke cases documented within the preceding 24 months were reviewed. Late presentation was defined as hospital presentation more than four hours after symptom onset. Frequencies, chi-square tests, and modified Poisson regression with robust standard errors were used to estimate adjusted prevalence ratios. Results: The analysis included 371 stroke cases. Of these, 317 (85.4%) presented after four hours, and the median time to presentation was 24 hours (interquartile range: 9-72 hours). Late presentation differed significantly by employment status, first-contact route, and pathway complexity at bivariate analysis. After adjustment, non-hospital first contact remained strongly associated with late presentation: patients whose first documented contact was non-hospital-based had almost 3 times the prevalence of delay compared with those whose first contact was hospital-based (adjusted prevalence ratio = 2.89; 95% confidence interval: 2.15-3.90; p < 0.001). Conclusion: Late presentation was pervasive in this tertiary hospital record cohort and was primarily associated with the initial direction of care-seeking. Stroke response interventions should emphasise immediate hospital presentation and strengthen urgent referral from non-hospital first-contact points.

Abstract: Background: Acute war-related traumatic wounds present significant challenges due to significant soft-tissue damage/loss, risk of contamination, limited access to antimicrobial therapy, need for delayed closure, and limited access to surgical and wound care. Negative Pressure Wound Therapy (NPWT) has been used effectively to reduce the volume of soft-tissue defects, edema, and infection in traumatic wounds, and to promote growth of healthy granulation tissue. However, conventional NPWT devices are costly and electricity-dependent, limiting their utility in conflict settings. Methods: This retrospective cohort study evaluated the use of PragmaVAC, a manually operated, electricity-independent NPWT device, in patients across three hospitals in Gaza with conflict-related wounds that were deemed by the treating surgeon to be unsuitable for primary closure. Secondary analysis was performed of clinical records of patients treated with the PragmaVac NPWT device to assess ability to achieve a primary outcome of wound bed with healthy granulation tissue, time to primary outcome, and rates of adverse effects. Secondary outcome of wound closure and closure method was also assessed. Results: Treatment with PragmaVAC manual NPWT was prescribed to 88 patients. Of those, 27 (31%) had incomplete documentation of their wound healing or were lost to follow up. The remaining 61 (69%) had complete documentation of their wound healing, complications, and final outcome with 59 (67%) successful closure and 2(2%) failure. Conclusion: The use of the PragmaVAC NPWT device provided a safe, effective wound care option to achieve wound closure for large conflict-related traumatic wounds in resource-limited settings. Future studies may further evaluate such use through prospective trials, evalutions of patients' experiences with manual NPWT, and evaluating outcomes beyond primary wound closure to include medium- and long-term complications, cosmesis, and cost of therapy.

Background Endoscopic pituitary surgery involves navigating high-stakes anatomy where complications, such as carotid artery injury, cause devastating morbidity. While computer vision AI offers potential for real-time anatomical recognition to mitigate these risks, successful translation requires rigorous human-factors and performance evaluation. We present the iterative development and preclinical evaluation of a surgeon-controlled, real-time AI-assisted navigation system. Methods Guided by IDEAL Stage 0 and DECIDE-AI frameworks, the study was conducted in two phases. Phase 1 was an exploratory study where surgeons used the system during high-fidelity simulated surgery and provided feedback via "Think Aloud" protocols and surveys. Following prototype iteration, a Phase 2 randomized crossover comparative trial was conducted with 19 neurosurgeons (15 trainees, 4 experts) performing high-fidelity simulated tumour resections with and without AI assistance, separated by a minimum 2-week washout. The primary outcome was surgical technical performance (OSATS). Workload, educational value, usability, trust, and implementation outcomes were also assessed. Results Phase 1 informed hardware, model, and interface refinements, including optimized pedal-controlled overlays and prediction confidence metrics. In the comparative trial, AI assistance significantly improved overall technical performance (OSATS 19.79+/-4.06 vs. 17.32+/-4.11; p=0.027). This gain was experience-dependent; AI significantly augmented trainee performance (19.20+/-3.76 vs. 16.60+/-3.78), narrowing the proficiency gap, while expert performance remained high and stable. 100% of participants identified the system as a useful training tool. However, subjective workload was significantly higher in the AI arm (SURG-TLX 26.42+/-9.56 vs. 22.26+/-7.81; p=0.014). Despite this, usability (SUS 75.13+/-14.31) and implementation feasibility, acceptability, and appropriateness scores were consistently high (means >4.4/5). Conclusions This study provides a stepwise process for real-time AI development using pituitary surgery as a high-stakes exemplar. The refined surgeon-centric AI system improves training and technical performance, particularly for trainees. Next steps involve first-in-human studies and further exploration of longer-term human factors such as over-reliance, cognitive overload mitigation and trust calibration.

During the COVID-19 pandemic, limited testing capacity and reporting delays complicated epidemic surveillance and decision-making in Mexico. We calibrated \textit{covidestim}, a Bayesian nowcasting model, to estimate the total SARS-CoV-2 infections from reported cases and deaths using Mexican surveillance data. Disease-progression distribution priors were calibrated using Mexico City records and validated through comparisons with national seroprevalence surveys, hospitalization data, and annual reported severe-case rates across all states. Using the reconstructed estimates of active infections, we implemented an event-based risk framework that quantifies the probability of encountering at least one infectious individual in gatherings of different sizes. This probability was subsequently translated into a four-level epidemiological traffic-light indicator and computed at both state and municipality levels. The resulting estimates revealed substantial spatial heterogeneity that is obscured by state-level aggregation, particularly in states with marked differences between urban and rural municipalities. To evaluate consistency with public-health indicators, we compared the proposed risk classification with the official Mexican epidemiological traffic-light system, considering interpretable gathering sizes relevant to public-health decision making. Weekly reports derived from this framework were delivered to policymakers in the State of Queretaro in Mexico, as an anticipation tool for school reopening and public-space management. This demonstrates that this Bayesian reconstruction of infections combined with event-based risk metrics can provide an interpretable and generalizable municipality-level complement to routine surveillance systems, particularly in regions with limited testing capacity and heterogeneous local transmission dynamics.

Abstract Background Malaria remains a major public health challenge in Ghana, despite recent reductions in cases due to various interventions. The endemicity of the disease varies across regions, influenced by diverse seasonal and temporal factors that support mosquito proliferation and malaria cases. This study used a Generalised Additive Models to explore the impact of weather conditions on malaria cases in Ghana. Methods Generalised Additive Models were used to examine the nonlinear effects of weather conditions on malaria cases. Monthly aggregated malaria cases from the District Health Information Management System II and average monthly rainfall and temperature data from the Ghana Meteorological Agency were analysed, covering 2012 to 2023. Regional Generalised Additive Models incorporating weather variables were developed, fitted, and validated against observed data using model diagnostics to identify the most suitable model for each region. Results The analysis revealed complex temporal patterns in malaria cases across Ghana, influenced by seasonal and long-term trends. Regions constituting the Coastal and Transitional Forest zones exhibited bimodal peak malaria seasons, while the Guinea Savannah showed a unimodal peak. Significant interactions between rainfall and temperature were identified, particularly in the Eastern region, where higher rainfall combined with temperatures around 27-28 {degrees}C were associated with higher malaria cases, reflecting the complex and region-specific nature of meteorological influences. Conclusions The findings point to the dynamic and heterogeneous nature of malaria caseloads in Ghana, emphasising the need for region-specific control strategies tailored to local climatic conditions. A key recommendation is the systematic integration of meteorological data into the National Malaria Data Repository to enable continuous monitoring of climatic influences and support timely, evidence-based intervention decisions. Future research should incorporate socio-economic factors, intervention coverage data, vector surveillance, and demographic characteristics into mathematical modelling frameworks for a more comprehensive understanding of malaria cases in Ghana.

Large language models (LLMs) such as ChatGPT are rapidly reshaping healthcare education and simulation-based training in non-technical skills (NTS), yet no bibliometric analysis has mapped this landscape. We searched seven open-access databases (OpenAlex, PubMed, Europe PMC, Crossref, Semantic Scholar, CORE, DOAJ) for English-language publications from January 2020 to March 2026. From 100,277 initial records, a sequential keyword funnel yielded 830 candidate papers, which were screened by 83 independent Claude Sonnet 4.6 AI agents applying pre-specified inclusion criteria (PRISMA-trAIce compliant; Cohen's kappa = 0.86 pre-reconciliation, 1.0 post-reconciliation). The final AI-verified corpus comprised 551 papers with a compound annual growth rate of 109%, contributions from 2,398 authors across 279 journals in 58 countries, and an h-index of 41. ChatGPT dominated the model landscape (46% of papers), with open-source models virtually absent. Virtual patient chatbots were the leading simulation modality (106 papers). Among NTS domains, communication (145 papers) and decision-making (135 papers) were most studied, whereas teamwork, leadership, situational awareness, and crisis resource management were markedly underrepresented. Only 6 urology-relevant papers were identified, none examining LLM integration within boot camp training formats. The field is growing at extraordinary pace but remains concentrated in a narrow range of NTS domains and a single proprietary model. Critical gaps persist in team-based skills training, open-source model evaluation, and specialty-specific simulation. AI-assisted bibliometric screening using multiple independent agents is feasible, reliable, and scalable, offering a replicable methodology for mapping fast-evolving research fields.

Introduction Dysmenorrhoea is highly prevalent globally and interferes with engagement in education, work, social participation, and quality of life. Although evidence suggests that sociocultural beliefs influence how menstrual pain is understood and managed, relatively little research has explored dysmenorrhoea-related knowledge and beliefs within South Africa. This study aimed to (1) determine the frequency of dysmenorrhoea, (2) assess dysmenorrhoea-related knowledge and compare knowledge between menstruating and non-menstruating individuals, and (3) explore commonly held generational, cultural, and religious beliefs related to dysmenorrhoea in a South African university cohort. Methods We analysed data collected as part of a cross-sectional survey conducted among staff and students at a South African university. Participants completed demographic questions, items assessing dysmenorrhoea-related knowledge, and an adapted Working Ability, Location, Intensity, Days of Pain, Dysmenorrhoea (WaLIDD) questionnaire. Participants were also invited to provide free-text responses describing generational, cultural, and religious beliefs about dysmenorrhoea. Quantitative data were analysed descriptively and compared between menstruating and non-menstruating participants. Free-text responses were analysed using reflexive thematic analysis. Results A total of 863 participants completed the survey, including 578 current or past menstruators. The frequency (95%CI) of dysmenorrhoea was 75.4% (71.7-78.9). Most participants were classified as having moderate (53%) or severe (31%) dysmenorrhoea on the WaLIDD scale. Awareness of dysmenorrhoea was higher among participants who had menstruated than among those who had never menstruated (80.4% vs 55.3%, p<0.001). Most participants (85.1%) reported wanting more education about dysmenorrhoea and its impact. Reflexive thematic analysis of 246 free-text responses identified five themes: (1) menstrual pain is normalised, dismissed, and expected to endure, (2) reproductive meanings attached to menstrual pain, (3) moral, spiritual, and cultural interpretations of menstrual pain, (4) negotiating competing explanations for menstrual pain, and (5) managing and controlling menstrual pain symptoms. Across themes, dysmenorrhoea was interpreted through social, cultural, reproductive, spiritual, and biomedical frameworks that shaped how pain was understood, communicated, and managed. Conclusion Dysmenorrhoea is common in this South African university cohort, and is rarely understood as a purely biological symptom. Instead, menstrual pain is understood and managed through broader social, cultural, reproductive, moral, and biomedical narratives, which shape how pain is recognised, disclosed, legitimised, and treated. These findings highlight the importance of considering sociocultural beliefs alongside clinical factors when developing menstrual health education, support strategies, and healthcare services.

Introduction Indigenous peoples in Canada face persistent health inequities rooted in colonialism, systemic racism, discrimination and social exclusion, all of which operate with particular intensity within healthcare institutions. Despite a growing qualitative literature documenting the discrimination and stigmatisation of Indigenous people by healthcare professionals, no validated instrument existed in the Canadian context to measure the stigmatizing attitudes and behaviors of clinicians toward this population. Aim This study aimed to co-develop and validate an instrument using clinical case vignettes designed to capture the affective, cognitive, and behavioral dimensions of stigmatization of indigenous peoples. Method Following Boateng et al.'s three-phase scale development approach, a multidisciplinary team including Indigenous patient partners, researchers, clinicians, and measurement experts generated 244 items across three paired clinical vignettes addressing type 2 diabetes, chronic back pain, and depressive disorder. Each vignette was developed in two versions, one featuring an Indigenous patient (test) and one featuring a non-Indigenous patient (control), distinguished solely by name and origin. Content validity was assessed by an expert committee using a Content Validity Index. The instrument was subsequently administered to a sample of nurses and physicians from two canadian health institutions using a twelve-arm randomization design. Analyses were carried to assess the internal structure of the instrument, convergent and concurrent validity as well as internal consistency. Results Our results show that the instrument developed has good psychometric qualities, particularly in terms of internal consistency, concurrent validity and factor structure, which reflects the theoretical structure assumed. Concurrent validity of the tool with the M-PATAS scale demonstrated weak to moderate significant correlations. Developed through a participatory process centering Indigenous expertise and lived experience, this instrument constitutes a significant methodological advance in the study of racialized stigmatization in Canadian healthcare.

Background: In resource-limited settings, a critical bottleneck in cervical cancer prevention is the lack of practical strategies to triage high-risk human papillomavirus (HR-HPV)- positive women. Therefore, this study aimed to develop and internally validate a genotype-specific risk stratification model. Methods: A cross-sectional study enrolled 555 women in Cameroon. Data collection integrated cervical cytology and HPV genotyping using Abbott m2000rt and Sacace multiplex systems. An iterative modeling approach with bootstrap validation was used to develop the model and address model instability. HR-HPV genotypes were transformed into a hierarchical risk variable due to sparsity and integrated with significant predictors. The final model was translated into a scoring system, and the risk gradients and performances were evaluated at two thresholds. Data was analyzed using SPSS 27.0. Results: The mean age was 44.8 years, and the prevalence of HR-HPV was 26.5% (147/555). The final model, incorporating HPV categories, age, and tobacco, demonstrated moderate discriminative ability (AUC=0.702, 0.642-0.762) with a good calibration (Hosmer-Lemeshow {chi}{superscript 2}=4.05, p=0.399). The scoring system assigned women to risk groups based on their total scores which produced a clear monotonic risk gradient; the observed probability of high-grade lesions/cancer ranged from 15% (score 0) to >65% (score [&ge;]4). At a conservative threshold ([&ge;]4 points), 4.7% (26/555) of women were classified as high-risk, concentrating 46% (6/13) of cancers (positive predictive value[PPV]=58%) while a sensitive threshold ([&ge;]3 points) had 16.8% (93/555) high-risk, concentrating 77% (10/13) cancers (PPV=38%). Both thresholds maintained a high negative predictive value (>95%). Conclusion: This bootstrap-validated, risk-stratification tool is a proof-of-concept in resource limited settings that assigns HR-HPV-positive women to distinct management pathways using three variables. After refining through a longitudinal study and external validation, this scoring system can improve the efficiency of cervical cancer screening programs in low-resource settings.

Background. Lung metastases in osteosarcoma (OS) are the main cause of the death. The accuracy of the diagnosis of nodules by computed tomography (CT) of the lungs is critically important for determining the disseminated stage of the disease and planning surgical treatment. The use of artificial intelligence (AI) in the search for lung nodules increases the accuracy of diagnosis and reduces the chance of missing metastases. Objective: to evaluate the accuracy of lung nodules diagnosis in adolescents with OS using AI. Methods. A retrospective assessment of CT scans of adolescents with OS was performed. A pathological nodule with an average size of [&ge;]4 mm was considered a target finding. The diagnostic accuracy of an AI algorithm previously trained on an adult dataset was evaluated, and the number of false positives (FP) and false negatives (FN) was determined. Sensitivity, specificity, accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, and F1-measure were calculated. Based on the obtained results, the effectiveness of the algorithm was assessed. Results. 248 CT scans of adolescents with OS were evaluated. The following results were obtained: in 5 cases, the AI algorithm showed a FP result (2.02%), in 34 cases, it showed a FN result (13.71%), and in 209 cases, a correct result (both true positive and true negative) (84.27%). The diagnostic accuracy of the algorithm was 0.843 (95% CI 0.794-0.887). The application of the AI algorithm in the practice of an X-ray doctor in a specific clinical task would allow to increase the sensitivity from 0.805 to 0.891, while ensuring an absolute decrease in the number of FN results by 8.59% and a relative decrease by 44%. Conclusion. The obtained results confirm the practical value of the application of the AI algorithm and justify the implementation of AI-assisted systems in the diagnostic protocols for lung metastases in adolescents with OS.

Background Tobacco smoking during pregnancy increases the risk of preterm birth, small for gestational age (SGA), stillbirth, and longer-term adverse health outcomes. Globally, reducing smoking in pregnancy is a key public health priority, yet the organisation, accessibility, and effectiveness of cessation support varies substantially between countries and healthcare systems. Differences in policy implementation, resource allocation, and integration of cessation services into antenatal care influence uptake and success rates across diverse settings. In England, pregnant women are entitled to free smoking cessation support, however, service delivery varies across regions with mixed efficacy. While tobacco smoking is more prevalent in deprived communities, there is limited understanding of how, why, for whom, and under what circumstances these services are most effective, particularly in areas of social deprivation, such as the North East and Yorkshire. Objective To conduct a realist evaluation to understand how smoking cessation services support pregnant women in areas of social deprivation to stop smoking and reduce adverse perinatal outcomes. Methods This multi-site realist evaluation will be conducted across three NHS maternity services in West Yorkshire, England. The study comprises four iterative stages: (1) development of initial programme theories through realist-informed literature scoping and stakeholder consultation; (2) case study data collection including qualitative interviews with pregnant women (approximately 15-30) and staff (approximately 15-30); (3) analysis of routine anonymised maternity and neonatal electronic data collected over a one-year period; and (4) realist analysis to refine context-mechanism-outcome (CMO) configurations. Qualitative data will be analysed using realist logic supported by NVivo software. Quantitative data will be analysed using descriptive and inferential statistics to explore associations between smoking cessation engagement and perinatal outcomes. Ethics and dissemination Ethical approval was obtained through the UK Health Research Authority and a Research Ethics Committee prior to study commencement (IRAS 364173; REC reference number 26/SC/0020). Findings will inform recommendations to improve smoking cessation support for pregnant women in deprived areas. Results will be disseminated through peer-reviewed publications, conference presentations, and stakeholder engagement.

Essential medicines are one of the cornerstones of financial protection and health equity. The REMEDIAR Program is an initiative of the Argentine Ministry of Health aimed at ensuring free access to essential medicines for the uninsured at the point of care in primary healthcare centers (PHC). This study analyzes the financing, procurement, and distribution of this program over two decades (2002 to 2024). It evaluates how the program's capacity to navigate economic and political challenges ensured an uninterrupted supply of essential drugs at the primary healthcare level in a federal country where health services are devolved to provinces. We adopted a mixed-methods approach to examine the duality between international concessional loans and domestic treasury funding. Findings reveal that while international financing enhanced predictability and efficiency, reducing procurement timelines from 458 to 235 days, it also constrained domestic planning through external conditionalities. Conversely, while national centralized procurement achieved superior price efficiency and lower dispersion, it faced rigidities in adapting to local needs. Territorial distribution analysis confirms that REMEDIAR reduced access barriers for vulnerable households without formal insurance. However, the program entered a stabilization phase, failing to consolidate robust coordination with subnational policies, becoming entrenched in its own operational logic. The study concludes that program effectiveness depends not only on resource volume but on management quality. To guarantee long-term sustainability, transition to national financing requires profound institutional redesign. This must integrate operational capacities with federal coordination and domestic regulations, ensuring that the primary healthcare supply chain remains resilient to macroeconomic volatility and political shifts, aligned with sub-national strategies.

Introduction: Differentiated service delivery (DSD) models aim to reduce time healthcare providers spend with DSD clients, increasing time available for non-DSD clients. We measured nurses' time allocation and explored their experiences with DSD models in South Africa. Methods: We conducted time and motion observations and surveyed nurses at 24 public primary healthcare facilities across two SENTINEL study rounds (09/2022-07/2023 and 11/2023-07/2024). We report median time nurses spent by activity, model of care, and interaction type. Log binomial regression investigated factors associated with high direct nurse-client interaction (above median minutes) and extended work-days ([&ge;]9 hours), and estimated adjusted risk ratios (aRR). Survey questions were related to client care, additional time availability, and policy changes post DSD implementation, with key themes presented alongside illustrative quotes. Results: 176 nurses (88% female, median age 44) were observed for 344 working days; of these, 60 (34%) participated in the provider survey. Nurses spent a median of 293 minutes (53% of their work-day) on direct nurse-client interaction, 89 minutes (22%) on client-support or facility-related tasks, and the remainder on other activities including personal breaks. Time spent per client was similar across conventional care clients (11 [IQR: 8-15] minutes) but ranged between 9 (7-13) to 11 (8-15) minutes for DSD clients; number of direct nurse-client interactions did not differ meaningfully. Nurses at facilities with 2,000-3,999 total remaining on ART (TROA) (aRR 1.56, 95% CI: 1.02-2.37) and in urban areas (aRR 1.43, [1.08-1.89]) had more direct nurse-client interactions than those at facilities with <1,999 TROA and in rural areas, respectively. Nurses at facilities with 4,000+ TROA (aRR 2.22, [1.36-3.63]) and those observed in SENTINEL 3.0 (aRR 1.53, [1.13-2.07]) were more likely to work standard or longer workdays than those at lower TROA facilities (<1,999), those in SENTINEL 2.0 and urban areas. Nurses reported DSD models improved client care (90%), freed up time (60%), and changed clinic procedures and policies (60%). Conclusions: While DSD models did not significantly reduce direct nurse-client interaction time, nurses reported improved client care and gained additional time. DSD impact may vary by facility context. As DSD implementation expands, effective time reallocation may enhance facility performance and provider productivity.

Background: Endoscopic endonasal transsphenoidal surgery (EETS) requires navigation around neurocritical anatomy. Today, artificial intelligence clinical decision support systems (AI-CDSSs) can orientate surgeons, but clinician trust in AI remains unclear, limiting safe deployment. This study evaluates how modifiable design affects trust and performance in a real-world pituitary surgery AI-CDSS. Method: Online, 70 clinicians with pituitary surgery experience were randomised evenly to a Basic or Enhanced AI-CDSS which outline the sella on EETS operative video. The Enhanced group additionally received explanation of the model and previous publications, alongside confidence labels depicting outline reliability. Both groups annotated the sella on six video clips, first alone then with the optional AI-CDSS. Clips were ordered by declining AI performance, except for the final clip. Self-reported trust was measured using a 1-7 scale after each annotation, and performance was the DICE overlap between user annotations and the ground truth. Comparisons used Mann-Whitney U and permutation analysis. Results: Sixty-four participants (91%) finished the exercise (31 Basic, 33 Enhanced). When AI performed best, median trust was 5.00 in both arms (U=559, p=.521). However, when AI performed worst, trust was significantly lower for the Enhanced group (3.00 vs 3.67, U=668, p=.035), sustained in the final clip (3.67 vs 4.33 U=687, p=.019). User performance improved with the AI-CDSS, but with no significant difference between the groups on the best or worst AI performing clips. Nevertheless, for the best AI, senior clinicians had higher median performance in the Enhanced group (0.95 vs 0.90, U=75, p=.066). There was also less dispersion in the Enhanced group when AI was inaccurate (IQR: 0.07 vs 0.21, p=.004). Conclusion: Interface design can improve trust calibration in a surgical AI-CDSS and may increment performance in seniors when AI is accurate, and consistency when AI is inaccurate. In future, these features may form important safety checks during translation to the operating room.

Background: Olfactory dysfunction (OD) in children remains underdiagnosed and poorly characterised. Despite its known impacts on nutrition, quality of life, safety awareness, and psychosocial development, no standardised diagnostic or management pathway currently exists for paediatric OD. This study aimed to characterise global practice patterns and identify diagnostic and therapeutic challenges unique to paediatric care. Methodology/Principal: A 44-item cross-sectional online survey was distributed to a verified international network of paediatric otolaryngologists across 36 countries via a closed professional platform. The survey assessed five domains: diagnostic practices, management protocols, technology and innovation, education and training, and barriers to effective care. Regional grouping was used to facilitate meaningful statistical comparisons. Categorical variables were evaluated using chi-square tests, with odds ratios and 95% confidence intervals reported for significant findings. Results: Of 351 potential participants, 167 responded (47.6% response rate). Most respondents (83%) reported seeing children with OD, yet 95% saw fewer than ten such patients annually. Psychophysical testing was never performed by 54.8% of respondents, while 88.4% routinely ordered cross-sectional imaging. Testing frequency increased significantly with patient age (Cochran's Q p<0.001). The most common barriers to objective testing were insufficient training (44.3%), time constraints (29.9%), and funding limitations (28.1%). Multidisciplinary collaboration was negligible. Significant regional variation was observed across most practice domains. Conclusions: Paediatric OD care is characterised by functional underinvestigation, fragmented multidisciplinary collaboration, and systemic educational gaps. These findings support urgent development of standardised clinical guidelines, age-appropriate validated assessment tools, and formal interdisciplinary care pathways.