This study aims to evaluate primary care providers' adherence to the standard of measuring blood pressure for people aged 35 or above during their initial visit, as per Chinese guidelines, and to identify factors affecting their practices. We developed 11 standardized patients (SP) cases as tracer conditions to evaluate primary care, and deployed trained SPs for unannounced visits to randomly selected providers in seven provinces of China. The SPs used a checklist based on guidelines to record whether and how blood pressure was measured. Data were analyzed descriptively and regression analysis was performed to examine the association between outcomes and factors such as provider, patient, facility, and clinical case characteristics. The SPs conducted 1201 visits and found that less than one-third of USPs ≥35 had their blood pressure measured. Only 26.9% of migraine and 15.4% of diabetes cases received blood pressure measurements. Additionally, these measurements did not follow the proper guidelines and recommended steps. On average, 55.6% of the steps were followed with few providers considering influencing factors before measurement and only 6.0% of patients received both-arm measurements. The use of wrist sphygmomanometers was associated with poor blood pressure measurement. In China, primary care hypertension screening practices fall short of guidelines, with infrequent initiation of blood pressure measurements and inadequate adherence to proper measurement steps. To address this, priority should be placed on adopting, implementing, and upholding guidelines for hypertension screening and measurement. 10.13039/501100001809 National Natural Science Foundation of China , 10.13039/100009131 Swiss Agency for Development and Cooperation , Doctoral Fund Project of Inner Mongolia Medical University, China Postdoctoral Science Foundation.

Introduction: Grandparents' involvement in grandchild caregiving has become an increasingly common social phenomenon worldwide. Compared with non-caregivers, grandparents' depression may be influenced by caregiving responsibilities. However, existing studies have reported inconsistent findings regarding this association. Methods: Following the PRISMA-ScR guidelines, this scoping review systematically mapped the existing literature on the relationship between grandchild caregiving and grandparents' depression. Eight academic databases were searched, yielding 3,174 records. After four screening stages, 30 eligible studies were included for data extraction, analysis, and synthesis. Results: The evidence revealed that the effects of grandchild caregiving on grandparents' depression are complex and context-dependent. Differences in caregiving definitions and measurements, grandparent and grandchild characteristics, family structures, and national or cultural contexts contributed to inconsistent results. Six major research gaps were identified: (1) geographical and methodological biases; (2) limited diversity in caregiving measurement; (3) lack of lineage-based analysis (maternal vs. paternal grandparents); (4) insufficient attention to grandchild characteristics; (5) reliance on single depression measures; and (6) inadequate exploration of mediating and moderating mechanisms. Discussion: Future research should expand geographic coverage, especially in developing regions, and adopt qualitative or mixed-method approaches. It should also diversify caregiving measurements, include lineage-based comparisons, integrate grandchild characteristics, use multidimensional depression tools, and apply theoretical models to explore how caregiving influences mental health. This review is limited by language (English-only publications), exclusion of gray literature, and heterogeneity among included studies, which may reduce comparability.

Geographical accessibility is important for promoting health equity, and calculating it requires the locations of all existing healthcare facilities in a region. Authoritative location data collected by governments is accurate but mostly not publicly available, while point-of-interest (POI) data from online sources, such as Baidu Maps and AutoNavi Maps are easily accessible. However, the accuracy of the latter has not been thoroughly analyzed. Taking Baotou, a medium-sized city in China, as aneample, we assessed the suitability of using POI data for measuring geographic accessibility to healthcare facilities.We computedthe difference of geographic accessibility calculated based on POI data and that on authoritative data.Logistic regression and a multiple linear regression model was applied to identify factors related to the consistency between the two data sources. Compared to authoritative data, POI data exhibited discrepancies, with completeness of 54.9% and accuracy of 63.7%. Geographic accessibility calculated based on both data showed similar patterns, with good consistency for hospitals and in urban areas. However, large differences (>30 minutes) were shown in rural areas for primary healthcare facilities. The differences were small regarding to population- weighted average accessibility (with slight underestimation of 3.07 minutes) and population coverage across various levels of accessibility (with differences less than 1% of the population) for the entire area. In conclusion, POI data can be considered foruse in both urban areas and at the level of entire city; however, awareness should be raised in rural areas.

Introduction: Although AI-assisted diagnostic systems are increasingly deployed in primary care globally, evidence from multiple countries indicates that deployment does not necessarily ensure clinical uptake. Resource-constrained and dispersed regions face implementation challenges, yet the mechanisms remain unclear. This mixed-methods protocol employs the Consolidated Framework for Implementation Research (CFIR) to systematically identify barriers and facilitators to AI implementation in primary care in such settings. Methods and analysis: This study adopts a sequential mixed-methods design. Phase I involves semi-structured in-depth interviews with multi-level stakeholders (defined by the 7P principle: Patients, Providers, Purchasers, Payers, Public, Policymakers, and Product suppliers), including primary care clinicians, facility managers, external administrators, and vendor staff. Purposive and maximum-variation sampling will cover diverse geographic areas and usage levels (high, low, and non-use). The interview guide will be developed based on the CFIR 2.0 domains. Interviews will be audio-recorded, transcribed verbatim, thematically analysed, and mapped to CFIR 2.0 constructs. Phase I will recruit professional stakeholders and patients until information saturation is reached. Phase II comprises a cross-sectional survey of primary care patients targeting a minimum of 482 valid responses, to identify and quantify patient-level acceptance of AI-assisted diagnosis and its influencing factors, informed by patients’ perspectives identified in Phase I. Phase III integrates findings from the first two phases and applies the Behaviour Change Wheel (BCW) and Implementation Mapping to formulate context-sensitive optimisation strategies. Ethics and dissemination: This study has been approved by the Medical Ethics Committee of Inner Mongolia Medical University (approval number: YKD202602099). All participants will provide written informed consent prior to data collection. Findings will be disseminated through peer-reviewed journal publications, national and international conference presentations, and policy briefs submitted to local health authorities. Beyond the pastoral regions of China, this protocol aims to generate an evidence base and a replicable methodological template (integrating CFIR 2.0, COM-B, and Implementation Mapping) that may inform the scaled implementation of AI in comparable resource-constrained and geographically dispersed health systems. The transferability of the findings to settings such as those in Central Asia, sub-Saharan Africa, and Small Island Developing States will be explicitly assessed as a research question rather than assumed.