Search Results (8,948)

The rapid evolution of Large Language Models (LLMs) has opened new frontiers in cybersecurity automation, enabling intelligent agents capable of multi-step reasoning, tool invocation, and autonomous decision-making across complex security tasks. While individual applications have emerged across threat intelligence, vulnerability assessment, penetration testing, and security operations center (SOC) automation, a systematic understanding of the LLM-based agent paradigm in cybersecurity—encompassing both single-agent and multi-agent architectures—remains lacking. This paper presents a systematic literature review following PRISMA guidelines, identifying records through 59 structured web-search queries whose results resolve predominantly to arXiv, Semantic Scholar, the ACM Digital Library, IEEE Xplore, USENIX, MDPI, SpringerLink, and Elsevier ScienceDirect, supplemented by citation chaining, for works published between January 2022 and April 2026; the full query record is published with the paper. We applied structured inclusion and exclusion criteria and classified 59 primary studies along five dimensions: security function, agent architecture pattern, knowledge augmentation strategy, human-in-the-loop posture, and evaluation rigor. Our analysis reveals that penetration testing and threat intelligence are the most extensively studied domains, while incident response and compliance verification remain critically underrepresented. Penetration testing alone accounts for over half the corpus (50.8%). Single-agent tool-calling remains the most prevalent architecture (30.5% of studies), whereas centralized multi-agent orchestration—present in 18.6%—yields the strongest reported performance gains, up to 4.3× on zero-day exploitation; prevalence and performance therefore point in opposite directions. No included study achieves production-grade (E4) evaluation: the entire field currently rests on controlled laboratory assessments. An independent search of six bibliographic databases recovers 86.3% of the studies the primary search had surfaced (79.7% of the full corpus) while indicating a total eligible literature of roughly 400 studies, so the corpus is reported as a documented subset rather than an exhaustive census. We propose a unifying taxonomy, identify cross-cutting challenges including hallucination, prompt injection, and benchmark fragmentation, and outline open research directions with particular emphasis on multi-agent orchestration design. Financial sector applicability under DORA and the EU AI Act is treated as a documented evidence gap rather than a synthesis: the corpus’s only compliance and risk assessment study is also its only banking-specific system. Full article

Affective computing has become increasingly relevant in child–robot interaction (CRI), particularly in social robotics, emotion recognition, engagement assessment, and autism-related interventions. This systematic review with a critical and integrative synthesis analyzes 105 included studies to examine how affect is sensed, represented, processed, expressed, and evaluated in CRI. The literature search was conducted in IEEE Xplore, Web of Science, Scopus, and PubMed, following a systematic screening process guided by the review objectives. A descriptive and structured narrative synthesis was conducted considering publication characteristics, robot platform and morphology, target population, sensing modalities and observed affect-relevant features, affective constructs and representation models, computational and control mechanisms, robot affective expression, evaluation strategies, and remaining research gaps. The findings show a strong emphasis on ASD-related contexts, visually observable and behavioral features, facial emotion recognition, body movement analysis, and engagement assessment. The review also identifies important limitations, including reliance on camera-based affect recognition, comparatively limited use of physiological and other complementary sensing modalities, unclear alignment between robot roles and interaction strategies, insufficient reporting of robot emotional expressiveness and control mechanisms, and limited attention to explainability, data governance, and long-term ethical implications. Based on these findings, an integrative taxonomy of affective computing in CRI is proposed, comprising six interconnected dimensions: interaction context; sensing modalities and observed features; affective constructs and representation models; computational and control mechanisms; robot affective expression; and evaluation and adaptation strategies. Rather than treating these dimensions as entirely novel categories, the taxonomy consolidates and extends previously fragmented classifications into a child-centered representation of the affective interaction process. Overall, this review argues that affective CRI should move beyond automatic emotion recognition toward multimodal, embodied, developmentally appropriate, explainable, and ethically grounded robot interaction. Full article

Botswana provides a setting in which to examine pastoral power. Customary authority, Christian institutions, public-health programmes, and state law have each helped to shape the social regulation of gender and sexuality. This systematic review brings together Botswana-specific scholarship on the ways care, counselling, scriptural interpretation, and institutional support affect lesbian, gay, bisexual, transgender, intersex, queer, and other gender- and sexually diverse people. The review followed PRISMA 2020. Web of Science, Scopus, ScienceDirect, SpringerLink, and Google Scholar were searched from database inception to 15 May 2024, and a supplementary search of publisher sites, indexes, and citation networks was updated to 31 July 2026. Five independent sources met the eligibility criteria: one qualitative interview study, one field-informed country report, and three documentary, legal, church-historical, or biblical analyses. The CASP qualitative checklist and the JBI text-and-opinion checklist were used for design-appropriate appraisal. Across this small and varied literature, four patterns recurred: the moral authority assigned to scripture and culture; care that was conditional or incomplete; interaction among religious, health, legal, and civil-society institutions; and dialogical alternatives to exclusion. Evidence is strongest for public discourse, institutional positions, and reported counselling experiences; no included source directly observed a pastoral ritual. Religious care may offer belonging and support, but it may also carry expectations of conformity. Broader claims will require ethnographic and participatory research developed with, and responsive to, LGBTIQ+ Batswana. Full article

The present systematic review set out to reassess whether claims of energy-efficient deep learning in transportation infrastructure are supported by direct sustainability evidence. Deep learning models run in transportation systems on edge devices with a limited energy budget, and the literature labels them “green” or “energy-efficient”; the share of studies that support the label with measurement had not been quantified. Following PRISMA 2020, the Scopus, IEEE Xplore, and Web of Science databases were searched for the period from January 2020 to June 2026. Included were 721 studies applying Green AI techniques: pruning, quantization, knowledge distillation, lightweight architectures, TinyML, and dedicated accelerators. The review covers the transport domains of roads and ADAS, railway, connected and autonomous vehicles, and sensor networks. Studies were classified by the strongest efficiency evidence they report: a direct sustainability metric (energy, power, power efficiency, battery life, CO₂) or computational proxies. A direct metric is reported by 58 studies (8.0%); the share is a lower-bound estimate. Full-text verification of a stratified random sample of 34 Tier 2 studies found one study with a direct metric not stated in its abstract (2.9%); the sample-adjusted estimate of the share is 10.7% (95% confidence interval 8.5 to 21.8%). The evidence levels differ: 48 studies (6.7% of the corpus) report power or energy measured on the target hardware, two derive battery life from a measured energy budget, seven report modelled or simulated values, and one a macro-level CO₂ estimate. Railway contributes three studies. The largest measured reduction in energy per inference is 1961.8-fold (0.005 J on an FPGA against 9.77 J on a 95 W CPU); the largest modelled factor in the corpus is approximately 2400-fold (a memristor accelerator against an embedded GPU). Measured and modelled values are distinguished throughout the text. The studies that measure show that rigorous reporting is feasible; from the evidence presented follows the recommendation that an efficiency claim in transportation AI be supported by a direct metric measured or explicitly modelled on a named target platform. Full article

The economic impact of food fraud is difficult to quantify precisely, because fraud is structurally designed to evade detection; available estimates are indirect projections rather than direct forensic accounting and are commonly cited in the range of USD 10–15 billion annually. The integration of artificial intelligence (AI) with analytical instrumentation has generated a rapidly expanding body of research aimed at detecting adulteration, mislabeling, and substitution across food matrices. This systematic review examines the extent to which AI-assisted instrumental technologies contribute to food fraud prevention (as distinct from laboratory detection) and characterizes the structural factors that constrain real-world translation. A systematic search of the peer-reviewed literature published between 2021 and 2026 yielded 83 eligible records (80 primary studies and 3 review articles) after applying predefined inclusion criteria. Data were extracted into a structured seven-sheet workbook covering study characteristics, instrumental technologies, AI architectures, performance metrics, industrial-validation status, implementation evidence, and methodological quality. The corpus shows consistently high reported analytical accuracy under controlled laboratory conditions (median of extractable classification accuracies ≈ 99–100%; ≥95% in 86% of studies with an extractable value). At the same time, 68 of 83 studies (82%) reported no external validation, no study (0/83) achieved inter-laboratory validation, no study documented routine-monitoring application, and only one study reported testing in a genuine industrial environment. The most frequently featured platforms were NIR spectroscopy and electronic-nose arrays (each featuring in 30/83 studies, frequently in data-fusion combinations), followed by gas-chromatography-based systems (16/83) and hyperspectral imaging (13/83). Classical machine learning predominated (57/83 studies coded as classical ML, with a further 11 hybrid ML/DL designs and 12 deep-learning-only designs). A direct statistical comparison found no significant difference in reported accuracy between classical-ML and deep-learning studies (median 100% vs. 98.2%; Mann–Whitney U test, p = 0.16). A pre-specified test of the hypothesis that high reported accuracy is itself a marker of overfitting was not supported by the corpus: reported accuracy was not negatively associated with external-validation status (Fisher’s exact p = 0.51) or with methodological-quality score (Spearman ρ = 0.15, p = 0.23). Methodological quality was predominantly moderate (49/83 scored 3/5; 22 scored 2/5; 11 scored 4/5; one study scored 5/5), and 19/83 (23%) carried a high risk of bias. The review’s central observation—a measurable gap between demonstrated laboratory detection and evidenced real-world prevention—is well supported by the deployment, inter-laboratory, and routine-monitoring data. We deliberately separate this strongly evidenced conclusion from weaker inferences (e.g., the overfitting hypothesis) that the corpus cannot currently establish, and we outline a validation-driven, deployment-oriented research agenda. Full article

Shade coffee agroecosystems are recognized as biodiversity-friendly systems that make an important contribution to biodiversity conservation in tropical landscapes. This study synthesized the scientific evidence on biodiversity associated with Mexican coffee agroecosystems through a systematic review of literature published between 1990 and 2026. Following PRISMA 2020 guidelines, searches in ScienceDirect, SpringerLink, Web of Science, Scopus, and SciELO yielded 1424 records, of which 126 met the inclusion criteria. The selected studies were further classified according to research axes, methodological approaches, production-system comparisons, and ecosystem functions. Research efforts were strongly concentrated in Veracruz and Chiapas, while several coffee-producing regions remained poorly represented. Arthropods were the most studied group, followed by plants and trees, while mammals and birds were equally represented; fungi and herpetofauna received comparatively less attention. Scientific output increased compared with the early years of the study period but showed considerable temporal variation, particularly during the most recent decade. Despite these advances, relevant geographic and taxonomic gaps persist, particularly for fungi, soil microorganisms, and herpetofauna. The evidence indicates that shade coffee agroecosystems serve as valuable reservoirs of biodiversity and support ecosystem services, ecological connectivity, and landscape conservation. Future studies should focus on underrepresented regions and taxa and incorporate functional diversity, ecological interactions, and landscape-scale approaches to strengthen biodiversity conservation and sustainable management in Mexican coffee-growing landscapes. Full article

We introduce triproximity, a conceptual framework that organizes tree–soil greenhouse gas (GHG) interactions across three spatial dimensions: (i) horizontal distance from tree stems, (ii) vertical soil profile depth, and (iii) structural position relative to tree components including the stem itself as a gas conduit. This addresses a critical and previously unquantified methodological gap in the literature. Despite the inherent spatial heterogeneity of tree-based agricultural systems, where molecular oxygen gradients structured by root macropore networks, rhizosphere demand, and canopy-mediated moisture redistribution govern CO₂, N₂O, and CH₄ fluxes across distances of just a few meters from the stem, most studies report GHG emissions from single locations without documenting distance from trees, effectively assuming spatial homogeneity where none exists. Following PRISMA guidelines, we systematically reviewed 107 field-based studies identified through a Scopus search (December 2025) of tree-based systems published between 2010 and 2025. Only 37.4% of studies explicitly reported measurement distance from trees, a proportion that has not improved despite a nearly four-fold increase in publication volume since 2020. Through narrative synthesis, we show that CH₄ uptake follows the most consistent spatial response, with higher oxidation rates in the near-tree zone across diverse system types; N₂O responses are context-dependent and governed by competing substrate availability and moisture controls; and CO₂ fluxes show no universal spatial pattern yet respond predictably to specific proximity dimensions once the dominant source term is identified. Stem-level gas transport remains virtually unmeasured across the dataset, likely biasing ecosystem GHG budgets systematically. We propose a minimum triproximity-based sampling protocol for five major tree-based system types and call for journals to adopt spatial reporting as a minimum submission standard. This review was not pre-registered and received no external funding. Full article

Background: Digital innovations have transformed health professions education, with podcasting emerging as a flexible, learner-centered educational modality. Podcasts support asynchronous, mobile, and self-directed learning, enabling access to educational content beyond traditional classroom environments. However, evidence regarding their effectiveness, integration strategies, and impact on educational and practice outcomes remains fragmented. Aim/Objective: This review aimed to map the existing literature on the use of podcasting in nursing and midwifery education and continuing professional development (CPD), identify how podcasts are used, summarize reported benefits and limitations, and highlight gaps for future research. Design: Scoping review. Methods: A scoping review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Eligibility criteria were developed using the Population–Concept–Context (PCC) framework to define the review scope. A comprehensive literature search was performed across PubMed/MEDLINE, Scopus, Embase, CINAHL, Google Scholar, ProQuest, and OpenGrey to identify studies published in English between January 2005 and December 2024. Two reviewers independently screened titles, abstracts, and full-text articles against the predefined eligibility criteria. Data were extracted using a standardized charting form, and the included studies were synthesized using descriptive statistics and thematic analysis to map the characteristics and educational applications. They reported outcomes of podcasting in nursing and midwifery education. Results: Twenty-four studies were included. Podcasting was associated with enhanced learning flexibility, learner engagement, and knowledge retention in both academic and CPD contexts. It supported asynchronous and self-directed learning while reinforcing key concepts. Challenges included variable content quality, limited integration of assessment, and scarce evidence linking podcast use to clinical outcomes. Conclusions: Podcasting is a promising adjunct to nursing and midwifery education and CPD. Formal integration into curricula and professional development frameworks is recommended. Further research should focus on longitudinal outcomes, low- and middle-income settings, and impacts on clinical practice and interprofessional learning. Full article

The convergence of Large Language Models (LLMs) with the Internet of Things (IoT) is driving a transformative shift toward a continuous, context-aware smart healthcare ecosystem. Due to its novelty, existing research in this domain remains fragmented, leaving a critical gap in unified frameworks that synthesize domain applications, functional AI deployment roles, network architectures, and security boundaries. Following PRISMA 2020 guidelines, this paper presents a systematic literature review and quantitative analysis evaluating a selected corpus of 61 peer-reviewed and 14 preprint papers in this domain. Methodologically, we assess a novel hybrid article discovery strategy, finding that an AI-powered prompt-based literature search strategy achieves higher precision than traditional keyword-based Boolean queries (86% vs. 42%) on the evaluated search sample, which may reduce screening workloads. We found that the major limitation of AI-based literature search is non-determinism, which is also an inherent property of LLM-powered applications. To address this, we propose methodological guidelines for using an AI-assisted hybrid literature search strategy. Based on the selected literature, we establish a multi-layer taxonomy organizing the IoT-LLM advances in the healthcare domain across four pillars: application domain, LLM role, IoT device type, and architectural deployment pattern. Quantitative synthesis reveals a heavy research concentration in remote patient monitoring and personal health management (representing 59% of the corpus combined), primarily driven by the data accessibility of wearable sensors (64%). Cross-tabulation uncovers a distinct capability–constraint spectrum: cloud-based deployments lean on heavyweight state-of-the-art models (mainly GPT-family models) for complex semantic reasoning, whereas edge, federated, and blockchain-based hybrid systems leverage localized models (BERT and LLaMA families). Patient data privacy and reduced communication overhead were among the main reasons for choosing localized models. Crucially, our assessment reveals a pervasive neglect of LLM-specific vulnerabilities such as prompt injection and jailbreak attacks and a tendency to treat regulatory frameworks (e.g., HIPAA, GDPR) as design features rather than empirically validated compliance metrics. Finally, we propose an actionable future research agenda prioritizing multi-device system orchestration, emergency care integration, privacy-preserving LLMs, and deployment-scale clinical validation. Full article

Background: Depression is a major health issue among older adults and is frequently accompanied by chronic physical conditions. Conventional care of depression in older adults is delivered through primary care settings and often fails to address the complex interactions between mental health, physical illness, and social determinants of health in this population. Integrated care models, characterized by coordinated, multidisciplinary, and patient-centered approaches, have emerged as a promising strategy for improving depression outcomes in late life. Objective: This scoping review aimed to characterize integrated care models for managing depression in older adults. The review examined the structure and implementation of these models and evaluated depression-related outcomes. Methods: A systematic literature search was conducted in PubMed and Google Scholar. Studies published in English between 2006 and February 2026 were considered. Eligible studies included randomized controlled trials (RCTs), cluster RCTs, and quasi-RCTs that evaluated integrated care interventions targeting depression in adults aged 65 years or older or mixed adult populations that included older adults. One reviewer conducted the search and initial screening, and four additional reviewers evaluated the eligibility of full-text reports and extracted data using a structured template. Results were synthesized descriptively. Results: Seventeen studies met the inclusion criteria. Integrated care interventions were implemented across primary care clinics, community health centers, and home healthcare programs in several countries. Participants commonly had depression alongside chronic medical conditions, including diabetes, hypertension, heart failure, or chronic obstructive pulmonary disease. Integrated care models varied in staffing, therapeutic components, specialist involvement, delivery intensity, and comparator conditions. Most interventions incorporated multidisciplinary care teams; structured symptom monitoring; care coordination; behavioral or psychological interventions; and, in some studies, chronic disease management, social support, or technology-supported follow-up. Thirteen studies reported a favorable depression-related finding at one or more assessment points; however, benefits were not always sustained, and some studies reported null- or comparator-favoring findings. Conclusions: Integrated care for depression in older adults encompasses heterogeneous models implemented across diverse healthcare contexts. Common themes included multidisciplinary collaboration, coordinated follow-up, structured psychological or behavioral treatment, symptom monitoring, and integration of mental and physical health management. Favorable outcomes were reported in most studies, but findings varied by population, setting, comparator, intervention intensity, and follow-up duration. These findings may inform future program design, although the effectiveness of the integrated care models and the independent contribution of individual components remain uncertain. Full article

Background/Objectives: Fibromyalgia is a chronic pain condition characterized by widespread musculoskeletal pain, fatigue, sleep disturbance, cognitive dysfunction, and multisensory hypersensitivity. It is increasingly conceptualized as a heterogeneous nociplastic pain condition in which altered nociceptive processing interacts with dysfunctional pain regulation, neuroimmune mechanisms, and variable peripheral contributions. This integrative narrative review aims to synthesize current evidence on the major mechanisms underlying pain in fibromyalgia, with particular emphasis on central sensitization, descending pain modulation, neurochemical dysregulation, small-fiber pathology, neuroimmune processes, and psychobiological modulators. Methods: An integrative narrative review was conducted using iterative, mechanism-oriented searches of the biomedical literature, primarily in PubMed/MEDLINE and complemented by targeted bibliographic searches and reference tracking. Research published up to July 2026 was considered, with emphasis on human mechanistic studies, systematic reviews, meta-analyses, and landmark experimental evidence relevant to the major pathophysiological domains of fibromyalgia. Results: Central sensitization and altered nociceptive gain remain prominent mechanisms of pain amplification in fibromyalgia, but they do not fully account for the clinical phenotype. Evidence also supports impaired and heterogeneous descending pain modulation, neurochemical imbalance, neuroimmune activation, autonomic and stress-system dysregulation, and peripheral contributions, including small-fiber pathology in a substantial subgroup of patients. These mechanisms appear to interact rather than operate independently, while cognitive and emotional factors further modulate symptom severity, persistence, and functional impact. Conclusions: Fibromyalgia is best understood as a heterogeneous nociplastic pain syndrome arising from partially overlapping central, peripheral, neuroimmune, autonomic, and psychobiological mechanisms whose relative contribution varies across patients. Recognizing this mechanistic heterogeneity may improve phenotypic stratification, biomarker development, and the design of more individualized, mechanism-informed therapeutic strategies. Full article

Plant wearable sensors have emerged as a transformative technology for precision agriculture and plant phenotyping, enabling in situ, real-time, and continuous acquisition of physiological signals from plant surfaces or internal tissues. However, existing reviews have organized the literature by monitoring targets, sensing functions, or material platforms, without systematically comparing technologies from the fundamental dimension of the degree of intervention imposed on plants. Drawing on representative studies identified through a structured literature search, this review establishes a three-tier classification framework—invasive, minimally invasive, and non-invasive—and conducts a head-to-head comparison across six dimensions: signal characteristics, plant disturbance, long-term stability, manufacturing complexity, field deployability, and biosafety. The results reveal that invasive sensors (nanobionic probes, implantable microelectrodes, and organic electrochemical transistors) achieve nM–pM detection limits, yet wound responses generally limit their effective monitoring duration to the order of days; non-invasive sensors (flexible patches, strain sensors, and multimodal platforms) support weeks-to-months of continuous monitoring and are amenable to scaled deployment, but the indirectness of surface signals confines detection limits to the μM level; minimally invasive technologies (microneedle arrays and ultra-thin microelectrodes) offer a compromise between the two extremes. On this basis, a decision framework based on three-layer selection is proposed to guide technology selection across laboratory research, field deployment, and controlled environment agriculture. Future efforts should focus on standardized performance evaluation protocols, biodegradable self-powered systems, and the integration of invasive–non-invasive hybrid sensing networks with plant digital twins. Full article

Background/Objectives: Healthcare service quality is a critical dimension of patient safety and operational performance. Traditional assessment approaches are often limited in their ability to support prediction, which has increased interest in machine learning (ML) models. This systematic review assessed the effectiveness of ML algorithms in predicting healthcare service quality metrics, with emphasis on their applications, comparative performance and implementation challenges. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, CINAHL, and ScienceDirect for studies published between 1 January 2020 and 30 July 2025. Following title/abstract screening and full-text review, 49 studies were included. Studies were grouped into conventional/ensemble ML and deep learning categories based on the primary model class analyzed in each article. Results: The reviewed studies focused mainly on acute clinical and operational outcomes, especially length of stay (27.0%), mortality (23.0%), and readmission rates (18.0%), while subjective, patient-centered metrics received less attention. Conventional and ensemble ML models, particularly RF and XGBoost, were frequently reported, while deep learning models were used in more complex prediction tasks. Conclusions: The evidence suggests that well-validated and interpretable ML models can support healthcare quality prediction. However, important challenges remain regarding implementation, validation, generalizability, and data heterogeneity. Full article

The growing demand for sustainable and affordable housing requires construction systems that reduce material consumption, embodied environmental impacts, construction waste, and life-cycle costs while retaining the value of building components across multiple use cycles. This study presents a systematic scoping review and evidence map of circular economy strategies and reuse readiness in modular sustainable and affordable housing. A structured literature search covering publications from 2016 to 2026 identified 283 records, of which 49 studies were included following screening and eligibility assessment. The included studies were coded according to circular economy strategies within the 0R–10R framework, building life-cycle stages, modular technology typologies, sustainability and affordability indicators, and reuse-readiness attributes. The evidence map shows that the literature remains predominantly focused on life-cycle assessment, carbon emissions, waste reduction, reuse, and recycling. In contrast, early-stage circular strategies, including refuse, rethink, reduce, and redesign, as well as repair, refurbishment, remanufacturing, social value, life-cycle affordability, and operational reuse readiness, remain insufficiently examined. The findings further demonstrate that prefabrication or modularity alone does not ensure circularity. High reuse readiness depends on demountable connections, non-destructive disassembly, component standardization, durability, repairability, material traceability, reverse logistics, and credible second-life scenarios. Based on the mapped evidence, this study develops an evidence-informed Integrated Digital Circular Modular Ecosystem pathway that connects circular design, material passports, reuse-readiness assessment, BIM and digital twins, component grading, reverse logistics, circular marketplaces, and life-cycle performance evaluation. By integrating reuse readiness, life-cycle affordability, and digital traceability within a single evidence-informed pathway, the study provides a structured basis for researchers, policymakers, and industry stakeholders to evaluate and advance modular housing beyond construction efficiency toward long-term circularity and component value retention. As a conceptual synthesis, the pathway requires further validation through expert assessment and application to actual modular housing projects. Full article

Background/Objectives: Quality indicators are essential for evaluating and improving primary health care (PHC), but existing indicator sets differ considerably in their definitions, development methods, data requirements, and applicability across healthcare systems. This scoping review aimed to identify and synthesize quality indicators used to assess PHC services, describe the principal areas and methodological approaches represented, and identify persistent measurement gaps. Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. PubMed/MEDLINE, Embase, and CINAHL were searched from inception to December 2025, with an updated search conducted on 27 August 2026. Targeted grey-literature searches of World Health Organization, United Nations Children’s Fund, and Organisation for Economic Co-operation and Development sources were also undertaken. Two reviewers independently screened the records and assessed potentially eligible full texts. Data were synthesized narratively and interpreted using the Donabedian structure–process–outcome framework, the seven World Health Organization dimensions of healthcare quality, and PHC-specific functions and content areas. Results: Twenty-three sources were included, comprising empirical studies, indicator-development and consensus studies, methodological reviews, national quality-improvement programmes, and international performance-measurement frameworks. Recurring areas included chronic disease management, preventive care, medication safety, access, continuity, service delivery, patient safety, efficiency, and healthcare utilization. Patient-reported outcomes and experiences, equity, social determinants of health, structural capacity, and broader outcomes meaningful to patients and populations were less consistently represented. Substantial heterogeneity in indicator definitions, reporting levels, data sources, and methodological approaches limited direct comparison across frameworks. Conclusions: PHC quality measurement remains extensive but fragmented. Future frameworks should balance structure, process, and outcome indicators, incorporate outcomes that matter to people, and combine standardized core measures with context-specific, feasible, valid, and actionable indicators. Full article