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The development of valid and reliable health questionnaires relies on respondents' accurate comprehension of items. The thinking-aloud (TA) technique, where participants verbalize their thought processes while answering survey questions, offers a powerful means to explore cognitive mechanisms underlying response behaviour. Despite its wide use, there is no consensus on how TA protocols should be designed, conducted, and analysed. This scoping review aimed to map methodological approaches applied to TA in health-related questionnaire development and validation, describe their variability, and critically examine current methodological practices to inform future research. PubMed, Scopus, and Embase were searched on July 17, 2025, without time limits. Eligible studies were original articles in English applying TA for questionnaire validation, including assessment of content, face, construct validity, or comprehensibility. Two reviewers independently screened records and extracted data using a standardized charting form. An operational criterion was used to judge the completeness of methodological reporting. Data were synthesized narratively and tabulated to describe study purposes, TA typologies, analytical strategies, and theoretical frameworks. From 1,678 retrieved records, 210 studies met inclusion criteria, of which 84 provided complete methodological descriptions. Most of the 84 studies involved patients (48%) and used concurrent TA (48%), with frequent prompting (55%) and probing (48%). Sessions were mainly face-to-face (71%), audio recorded (64%), and transcribed verbatim. Thematic (38%) and content (26%) analyses predominated, often supported by NVivo. The Survey Response Model was the most cited theoretical framework (26%), though 22% of studies lacked any theoretical reference. Coding strategies were mixed (36%), and about half of the studies reported questionnaire revisions after analysis. TA remains a versatile but methodologically fragmented approach in questionnaire validation. The lack of standardization limits comparability and interpretability across studies. Greater transparency in reporting and explicit theoretical framing are recommended to improve methodological rigor. The review followed the PRISMA-ScR framework and was prospectively registered on OSF (DOI: https://doi.org/10.17605/OSF.IO/AZY3).

The main aim of this article is to significantly expand the frame model in order to analyze scientific theories by means of what I call theory frame modules (TFMs). I will illustrate the notion of TFMs through three scientific theories (Aim 1), each employing different scales of measurement that will be analyzed by corresponding TFMs (Aim 2). To this end, I will propose a frame-based definition of theoretical concepts (Aim 3). In the next step, I will focus on intertheoretical relations between TFMs and provide frame-based definitions of the relations of specialization and theoretization (Aim 4). Frames offer a means of reconstructing recursive structures. To provide a more differentiated perspective on recursion within frames, I will distinguish between recursion in the narrow sense and recursion in the broad sense (Aim 5). Finally, I will propose a frame-based distinction between an operationalist and a unificationist approach to scientific concepts, the latter of which allows for the introduction of theoretical concepts that designate a common cause of different empirical phenomena (Aim 6).

Simultaneous trajectory tracking and obstacle avoidance are critical capabilities for unmanned underwater vehicles (UUVs). However, implementing these tasks on digital processors inevitably introduces discretization errors and time delays. Most existing continuous-time methods suffer from significant performance degradation when directly applied to discrete-time systems. Therefore, achieving high-precision control in the discrete-time domain remains a considerable challenge. To address this issue, a robust discrete-time dual-loop control architecture is proposed in this paper. First, a discrete-time distributed model predictive control (DMPC) scheme serves as the outer-loop kinematic controller, solving an online optimization problem to generate collision-free velocity commands. To guarantee closed-loop stability, discrete LQR-based terminal constraints are strictly incorporated. Furthermore, slack variables are introduced to dynamically relax these constraints during obstacle evasion maneuvers. This strategy effectively resolves the inherent conflict between theoretical stability and recursive feasibility in cluttered environments. Second, a discrete-time sliding mode controller (DSMC) is developed for the inner dynamic loop, where a specialized reaching law is designed to strictly confine state fluctuations within a minimal quasi-sliding mode band. In addition, a discrete disturbance observer (DDOB) is introduced to estimate and compensate for the effects of time-varying ocean current disturbances. Extensive simulation results demonstrate that the proposed framework achieves superior tracking accuracy, high computational efficiency, and reliable obstacle avoidance. Furthermore, rigorous theoretical analysis ensures global system stability under discrete-time implementation constraints.

Dental materials science, a foundational discipline in stomatology, faces persistent challenges in traditional pedagogy: voluminous content, outdated textbooks, and a disconnect between theoretical knowledge and clinical application. Students often memorize material parameters passively without developing the capacity to translate this knowledge into real-world clinical decision-making. Blended learning platforms such as Moodle offer a promising avenue to bridge this gap through clinically oriented supplementary instruction. This study aimed to construct and evaluate a Moodle-based, clinically oriented online supplementary course in dental materials, and to examine its differential effects on students at different educational stages. A prospective quasi-experimental design with pre- and post-test comparisons was employed. A total of 60 undergraduate dental students were enrolled, comprising 30 fourth-year students (pre-clinical, G4) and 30 fifth-year students (clinical internship, G5). All participants completed a 4-week Moodle-based online supplementary course featuring H5P interactive videos, virtual case decision-making modules, and structured peer-review exercises. Learning outcomes were assessed via a self-developed Clinical Case Test (CCT) scored independently by two clinical teachers (ICC > 0.8), a modified Dundee Ready Education Environment Measure (DREEM) inventory (Cronbach's α = 0.947), and a course satisfaction questionnaire (Cronbach's α = 0.927). Statistical analysis was performed using paired and independent samples t-tests with α = 0.05. Both groups perceived the educational environment positively (overall DREEM score rate 82.35%). After the intervention, G4 students demonstrated a significant improvement in case analysis scores (24.30 ± 4.34 to 28.33 ± 6.76, P = 0.0083), while total CCT scores showed marginal significance (70.83 ± 7.79 to 75.73 ± 11.46, P = 0.0583). G5 students showed no significant change in total scores (75.77 ± 8.17 to 77.53 ± 9.86, P = 0.4532) but significant improvement in case analysis (29.57 ± 4.80 to 33.87 ± 4.97, P = 0.0012). Post-intervention, G4 CCT scores were statistically indistinguishable from G5 baseline scores (P = 0.5170), indicating that the intervention elevated pre-clinical students to near-internship levels. DREEM subscale comparisons revealed G4 scored significantly higher than G5 in Students' Perception of Learning (SPL: 47.40 ± 6.66 vs. 43.30 ± 9.00, P = 0.050) and Social Self-Perception (SSP: 13.03 ± 1.99 vs. 11.80 ± 2.55, P = 0.042). Course satisfaction was high overall (G4: 87.77 ± 11.74; G5: 83.20 ± 13.33), with 71.67% of students affirming that blended teaching most helped with "knowledge integration and clinical transformation." Moodle-based clinically oriented blended teaching enhances dental students' clinical case analysis competence, particularly for students at the pre-clinical stage. The platform effectively bridges the gap between theoretical knowledge and clinical application, positioning it as a valuable supplementary tool for clinical pre-education. Platform design should be dynamically adapted to students' clinical experience levels.

Type 1 diabetes poses substantial self-management challenges for children. Gamified interventions are a promising strategy to support this population, yet evidence on their design, implementation, and reported outcomes remains scattered. This scoping review aims to systematically map the international evidence on gamified interventions for children with T1DM, focusing on their characteristics, delivery, and outcomes. Following the Arksey and O'Malley framework, this scoping review systematically searched six databases (PubMed, CINAHL, Embase, Scopus, Cochrane Library, Web of Science) from January 1, 2010, to January 25, 2026. We also examined reference lists and performed citation tracking. Twenty-three of 762 retrieved articles were included. Interventions were primarily delivered via mobile applications (52%) and websites (17%). The most common gamification elements were goal setting, challenges, and fun (each 96%); social features were less frequent (35%). Most studies (70%) lacked an explicit theoretical framework, and intervention durations varied widely. The interventions demonstrated benefits for glycemic control, self-management, knowledge, and psychological distress, but inconsistent effects on quality of life. They were generally feasible, usable, and acceptable. Gamified interventions represent a promising approach to T1DM management in children, aligning well with their developmental needs. However, current studies often lack a theoretical foundation and evidence of sustained benefits. Future work should prioritize theory-driven design and rigorous long-term evaluation. This review protocol is registered on the Open Science Framework (OSF) and accessible via the following link: https://doi.org/10.17605/OSF.IO/MN6AE.

Some commonly prescribed antidepressants inhibit CYP2D6, an enzyme that metabolizes tamoxifen into its active metabolite, endoxifen. This inhibition could theoretically decrease the efficacy of tamoxifen, which could hypothetically lead to higher breast cancer recurrence rates and mortality. There is inconsistent data reported on the potential drug-drug interaction between concomitant use of tamoxifen and CYP2D6-inhibiting antidepressants. We investigated prescribing patterns of concomitant tamoxifen and antidepressant medications. Our study population included adult women ≥ age 18, with stage 0-IV breast cancer who were treated at the Cleveland Clinic, received tamoxifen, and were concurrently prescribed at least 1 antidepressant medication. Patients diagnosed between 2016 and 2021 were identified from a tumor registry database. Among the 405 included patients, 74.8% were taking at least 1 antidepressant before initiating tamoxifen, while 25.2% were started on antidepressant therapy after beginning tamoxifen. Following tamoxifen initiation, 66.3% of patients who had been on an antidepressant continued with the same medication. Among those who switched antidepressants, 16.8% changed to a serotonin-norepinephrine reuptake inhibitor (SNRI), 12.8% changed to a different selective serotonin reuptake inhibitor (SSRI), and 2.6% changed to another class of antidepressant. There is awareness of tamoxifen and antidepressant interaction among healthcare providers. However, differences in prescription patterns across providers exist. Individualizing care and, when appropriate, opting for antidepressants with weak or no CYP2D6 inhibition that are equally effective in treating mood disorders or other conditions may be important. While no adherence or outcomes data are presented here, a collaborative, interdisciplinary approach may help support consistent and effective oncologic and behavioral health patient care.

Accurately identifying "hot-spot pathways" in fishery science and technology (S&T) innovation is critical for food security, economic development, and ecological sustainability. Traditional technology foresight methods struggle to capture complex, dynamic evolutionary patterns in S&T innovation networks. Drawing on Dosi's conceptual framework of technological trajectories as domain-inspired design heuristics-whereby Dosi's qualitative concepts provide structural guidance for model design rather than formal axioms that exhaustively capture the theoretical framework-this study proposes DTH-GNN (Documents-based Temporal Heterogeneous Graph Neural Network), integrating Graph Neural Networks with dynamic evolutionary analysis to identify potential hot-spot pathways. We construct a dynamic heterogeneous knowledge graph from multi-source data (2010-2024) encompassing 32,847 publications, 8,956 patents, and 1,856 projects. DTH-GNN combines an R-GCN-based heterogeneous encoder with a GRU-based temporal evolution module, achieving AUC = 0.934 and AP = 0.928 (after rigorous leakage assessment), significantly outperforming GCN, R-GCN, and EvolveGCN baselines. Information-theoretic analysis indicates that temporal features account for a substantial share of mutual information in link prediction (31.8%, 95% CI: [28.4%, 35.1%]), comparable to structural features (29.1%) and higher than attribute features (19.7%). Three high-potential pathways are identified and validated through expert evaluation (Krippendorffś α = 0.804): Smart Aquaculture, Green Seed Industry, and Ecological Fisheries. These findings provide data-driven scientific support for S&T investment prioritization in the fishery sector.

Antimony-based anodes have emerged as up-and-coming alloy-type anode materials for sodium-ion batteries (SIBs), owing to their high theoretical capacity and excellent electrical conductivity. However, they still suffer from severe issues such as substantial volume expansion during the sodiation-desodiation process. The novel antimony phosphate/carbon composites are designed through a multi-composited strategy in this work. The exceptional electrochemical performance of the synthesized phosphate/carbon composites arises from the synergistic combination of the unique physicochemical properties of antimony phosphate, the structural flexibility and high electrical conductivity of graphene oxide (GO), and the advantageous features of melamine-resin-derived nitrogen-doped porous carbon. For instance, the SbPO4@MFC/rGO-0.6 still maintains a high reversible discharge capacity of 201.6 mAh g-1, after 2000 cycles at a high current density of 2.0 A g-1. Density functional theory (DFT) calculations demonstrate that the optimized electronic structure of SbPO4@MFC/rGO enhances the interfacial interactions between antimony phosphate and the nitrogen-doped carbon matrix, thereby increasing the binding ability of the matrix with Na+. Furthermore, the SbPO4@MFC/rGO-0.6//Na3V2(PO4)3 full cell can manifest a high energy density of 168.2 Wh kg-1. In particular, SbPO4@MFC/rGO-0.6//Na3V2(PO4)3 delivers an energy density of 103.2 Wh kg-1 while maintaining the high power density of 794.1 W kg-1.

Constructing chiral organic long-persistent luminescence materials has garnered considerable attention owing to their extraordinary ultralong emission duration but has thus far achieved limited success. Herein, we present a straightforward approach to develop chiral chromophore engineered donor for exploring chiral exciplex system, which exhibits a blue circularly polarized exciplex emission peaked at ~ 440 nm, as well as an ultralong yellow organic long-persistent luminescence from the triplet emission of chiral chromophore with emission peak at 540 nm and ultralong duration of 90 minutes. Particularly, the exciplex system achieves mirror-symmetric chiroptical signal with the dissymmetry factor of 7.8×10-3. Theoretical and experimental analyses reveal that the phenomenon is attributed to the charge transfer process that facilitates exciplex formation, followed by effective energy transfer from the exciplex to the chiral chromophore. The developed exciplex enables the applications of multi-level information encryption and three-dimensional display objects. This work presents a significant insight into advancing organic chiral afterglow materials with ultralong duration, unlocking broad application potentials across various domains.

Autophagy is well established as a critical mechanism in plant stress responses and sexual reproduction, yet its molecular mechanisms during asexual reproduction remains poorly characterized. Deciphering the autophagy regulatory network in somatic embryogenesis (SE), a key model of asexual reproduction, holds substantial significance for advancing our understanding of plant cell totipotency and improving crop genetic transformation systems. This study aimed to identify and characterize key autophagy-related regulators in cotton asexual reproduction, and to elucidate their transcriptional regulatory network. We performed transcriptome analysis across distinct ovule developmental stages in cotton, integrating subsequent molecular and cellular investigations. Functional characterization was conducted by generating overexpression lines coupled with comprehensive phenotypic and histological examinations. The molecular regulatory mechanism was elucidated using yeast one-hybrid assays (Y1H), electrophoretic mobility shift assays (EMSA), and dual-luciferase reporter assays. Autophagic activity at different SE stages was assessed by transmission electron microscopy (TEM), monodansylcadaverine (MDC) staining, and Western blot analysis. We identified GhATG18a as a potential autophagy-related regulator of somatic embryogenesis (SE), a model of plant asexual reproduction, based on transcriptome analyses across key SE developmental stages. Phenotypic characterization revealed that GhATG18a overexpression significantly enhanced callus formation and proliferation, accompanied by elevated autophagic activity throughout SE. Mechanistically, the transcription factor GhLBD18 directly binds the GhATG18a promoter and increases its transcriptional activity, thereby promoting callus formation and proliferation. These findings demonstrate that the GhLBD18-GhATG18a pathway accelerates early SE stages and shortens the transformation cycle by enhancing autophagy, providing a theoretical foundation for elucidating the role of autophagy in cell fate transitions and improving cotton genetic transformation systems.

Systemic inflammation is increasingly recognised as a key modulator of brain function, yet its impact on large-scale brain network topology remains incompletely understood. In particular, it is unclear whether inflammation alters the balance between functional segregation and integration, as captured by small-world organisation, and whether such effects are better detected using dynamic rather than static connectivity analyses. In this study, we conducted a secondary analysis of a previously collected dataset to examine the effects of experimentally induced inflammation on static and dynamic small-world topology using resting-state fMRI and graph-theoretical methods. Eighteen healthy male participants completed a double-blind, placebo-controlled, randomised crossover trial involving typhoid vaccination, a well-established model of low-grade systemic inflammation. Small-worldness was quantified across a fixed density range for both static and dynamic functional connectivity. No significant differences were observed in static small-worldness between conditions. In contrast, dynamic analyses revealed a significant reduction in mean small-worldness following vaccination. This effect was primarily driven by reduced clustering coefficient, with no change in characteristic path length, indicating a selective alteration in local segregation while preserving global integration. Dynamic state analysis identified two recurring connectivity states with distinct topological profiles. Although differences in fractional occupancy and dwell time were not statistically significant, participants showed a consistent tendency to spend less time in the high small-worldness state following inflammation. These preliminary findings indicate that inflammation modulates the temporal organisation of brain networks in ways not detectable using static approaches and are consistent with a metabolically efficient reconfiguration of network topology under inflammatory challenge.

This study explored the relationship between Artificial Intelligence (AI) literacy and decision-making quality, with employee-AI collaboration serving as the mediating variable, while also examining the moderating role of AI trust in this process. The research is based on the conservation of resources theory. A questionnaire survey was conducted among 435 employees of high-end manufacturing enterprises in the Yangtze River Delta and Pearl River Delta regions of China. Hierarchical regression analysis was employed to test the theoretical model. The research results indicate that AI literacy was positively related to decision-making quality. Employee-AI collaboration played a mediating role in the relationship between AI literacy and decision-making quality. Further analysis of the moderating effects also indicates that different modes of AI trust have different moderating roles. The relationship between employee-AI collaboration and decision-making quality was weaker for employees with high initial trust; however, the relationship between employee-AI collaboration and decision-making quality was stronger for employees with high post-task trust. This study provides a new theoretical perspective for understanding the relationship between AI literacy and decision-making quality, and offers empirical evidence for optimizing the design and management practices in human-AI collaborative systems.

The integration of game-based learning (GBL) into vocational education remains under-explored, and this research aims to explore the impact of a GBL module on learner achievement, foreign language enjoyment and learner autonomy based on the theoretical framework of Flow Theory. The quasi-experimental mixed-method design was adopted with an experimental group (60 students) and a control group (66 students). After the six-week intervention, the quantitative results revealed that the GBL module significantly enhanced learner achievement and foreign language enjoyment, but no statistical difference was observed in terms of learner autonomy. The qualitative findings highlighted the strengths of the GBL module in enhancing language skills, facilitating flexible and personalized learning, and also underscored the weaknesses such as insufficient game features and technological issues. These findings not only operationalized the Flow Theory to develop a GBL module tailored to the vocational language learning, but also provided empirical evidence for the potential of game-based learning to foster cognitive, affective and behavioral learning outcomes in the vocational setting.

This study proposes an integrated multi-model coupling framework for ex-ante carbon assessment in new urban district planning. The framework combines investment allocation, land-use carbon estimation, and traffic emission models within a unified analytical environment to simulate spatial and economic feedbacks that drive urban carbon emissions. Compared with conventional carbon assessment approaches, which often evaluate land use and transport as independent modules, the proposed framework introduces a rule-based coupling mechanism that dynamically links investment intensity, spatial configuration, and carbon output through iterative feedback. Each sub-model exchanges parameters via a shared data layer, enabling recursive interactions among economic input, land-use change, and mobility patterns. The framework advances urban carbon modeling through three key innovations. First, it establishes an investment-carbon elasticity mechanism that quantifies how capital concentration influences spatial emission patterns. Second, it formulates a rule-based symbolic coupling algorithm that enables cross-model parameter updating during iteration. Third, it develops a feedback-controlled carbon evaluation process that transforms traditional PSS from descriptive visualization tools into predictive decision-support frameworks. Applied to a representative new urban district, the framework demonstrates effectiveness in identifying low-carbon planning strategies under varying investment-intensity scenarios. By integrating economic, spatial, and environmental dimensions into a unified analytical logic, this research provides a scalable foundation for quantitative decision-making in sustainable urban transitions and contributes a theoretical model applicable to broader regional and national carbon-neutral planning frameworks.

There is a strong emphasis globally that health policy and practice can be improved by leveraging best available evidence in decision-making processes. The dynamic political context of policy means, however, that decision-making processes are highly complex. A growing field of inquiry examines how the evidence-informed approach impacts on and interacts with real-world policy development. Despite the growing recognition of the complexity of health policy development little is known about policy narratives underpinning evidence-informed decision-making. Analysis of policy narratives over time can reveal how recognition of policy problems and their corresponding solutions emerge and shift through a contestation of different ideas and interests and how they legitimise different political courses of action. The aim of this study is to explore policy narratives underpinning evidence-informed decision-making within Swedish healthcare, tracing their formulation and development from 1992 to 2024. The study is based on a textual analysis of Swedish government documents published 1992-2024. Drawing on theories of policy narratives, a thematic content analysis of 132 Swedish government-issued documents during this period was conducted. Four episodes with a dominant narrative in each emerged from the analysis. They differ in ideas connected to policy problems and solutions in healthcare and evidence-informed decision-making. Besides ideas on the substance of the policy solutions, the ideas include different assumptions about problems they are solving, how healthcare actors are connected to problems and solutions, values that motivate the solutions, and assumptions about mechanisms that will promote sound decision-making and resource use in healthcare. These four policy narratives include one of efficiency, provision of information, and deliberation; one of equality, data, and standardisation; one of integration, synthesis of perspectives, and collaboration; and one of responsiveness and the local context of decisions. The study offers insights into the governance of evidence-informed decision-making in healthcare. The findings contribute to a broader understanding of implications of the evidence-informed approach on healthcare policy, and how healthcare policy is shaped by competing ideas, values and assumptions. They also contribute to the theoretical debate on policy narratives, and discursive practice in general in healthcare governance.

Ovarian cancer (OC) is the most lethal gynecologic malignancy, presenting insidious onset and lacking specific biomarkers. Tumor-associated macrophages (TAMs) modulate immunity via M1/M2 plasticity, yet their heterogeneity and pro-metastatic mechanisms remain unclear. Using scRNA-seq, we compared immune profiles of OC and normal tissues, extracted TAM modules with hdWGCNA, and intersected them with DEGs to screen candidate genes Univariate/multivariate Cox and LASSO refined a seven-gene prognosis model (EPB41L2, AAK1, PRPF38B, RB1, GPR34, CLEC12A, BRD2) and established a nomogram. We also analyzed the DEGs' functional enrichment, immune signature, immune infiltration profile, and drug sensitivity. Unique immune cell infiltration landscape and gene expression profiles in the tumor microenvironment in OC were revealed. Based on the median risk score, OC patients were assigned to high-risk (HR) and low-risk (LR) groups, and the latter had far longer survival time than the former (P&#x2009;<&#x2009;0.0001), which was validated in GEO (P&#x2009;=&#x2009;0.018, P&#x2009;=&#x2009;0.0063). The HR group had significantly increased Dysfunction Score (P&#x2009;<&#x2009;0.01), MSI Score (P&#x2009;<&#x2009;0.01), ESTIMATEScore (P&#x2009;=&#x2009;0.045), and StromalScore (P&#x2009;=&#x2009;0.0031) rather than TumorPurity Score (P&#x2009;=&#x2009;0.045). The seven-gene prognosis model possesses a certain capability to predict survival rates and correlates with drug sensitivity and immunotherapy response. Identified immunoregulatory targets provide a theoretical basis for TAM-targeted treatments.

Although health messaging by social media influencers (SMIs) may have positive effects such as promoting awareness and decreasing stigma, concerns have been raised that influencer narratives may lack comprehensive or balanced information and may evidence a disconnect with accepted clinical guidelines, especially when it comes to mental health. The purpose of this scoping review was to describe the state of research on both content of and audience response to SMI mental health communication. A total of 2,746 records were retrieved from searches, among which 23 studies met the inclusion criteria. Studies have investigated a range of influencer types, mental illnesses, and social media platforms. Research on this topic is nascent, with the majority of studies having been published in 2024 or later. Across methodologies, the focus was on mapping the existing situation, analyzing SMI personal narratives of mental illness, identifying dialectical tensions for both SMI content creators and followers, and describing ethical dilemmas of SMIs. Few studies were theoretically based, and few of the concerns raised in the previous literature regarding either problematic content or negative behavioral impacts of SMI mental health messaging were empirically investigated. Thoughtfully designed longitudinal designs of key issues of concern articulated in the previous literature, as well as testing and development of appropriate theories, are needed going forward.

Women during emerging adulthood are disproportionately susceptible to sexually transmitted infections due to biological and socioeconomic vulnerabilities. Central to safe sex is the process by which women seek information regarding their male partners' intentions to use condoms, which is complicated by the gendered nature of sexual risks and the influence of sociocultural factors. Drawing on the theory of motivated information management, we examined how relationship quality, sexual relationship power, and sexual shame were associated with sexual risk information-seeking among Chinese emerging adult women in committed heterosexual relationships. Analyses of data from 294 participants showed that relationship quality and sexual relationship power were positively associated with outcome expectancy and efficacy assessments, subsequently contributing to more information seeking behaviors about condom use. Additionally, higher levels of sexual shame were associated with lower levels of efficacy assessments, and the effect of shame on efficacy was completely mediated by negative emotions, relationship quality, and sexual relationship power. These findings contribute to theoretical understandings of sexual health information management situated within specific relational and socio-cultural contexts and offer practical implications for improving sexual negotiation in Chinese cultural contexts.