Measures Of Trust Research Articles

Understanding Trust and Reliance Development in AI Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions

People are increasingly interacting with AI systems, but successful interactions depend on people trusting these systems only when appropriate. Since neither gaining trust in AI advice nor restoring lost trust after AI mistakes is warranted, we seek to better understand the development of trust and reliance in sequential human-AI interaction scenarios. In a 2 \({\times}\) 2 between-subject simulated AI experiment, we tested how model accuracy (high vs. low) and explanation type (human-like vs. abstract) affect trust and reliance on AI advice for repeated interactions. In the experiment, participants estimated jail times for 20 criminal law cases, first without and then with AI advice. Our results show that trust and reliance are significantly higher for high model accuracy. In addition, reliance does not decline over the trial sequence, and trust increases significantly with high accuracy. Human-like (vs. abstract) explanations only increased reliance on the high-accuracy condition. We furthermore tested the extent to which trust and reliance in a trial round can be explained by trust and reliance experiences from prior rounds. We find that trust assessments in prior trials correlate with trust in subsequent ones. We also find that the cumulative trust experience of a person in all earlier trial rounds correlates with trust in subsequent ones. Furthermore, we find that the two trust measures, trust and reliance, impact each other: prior trust beliefs not only influence subsequent trust beliefs but likewise influence subsequent reliance behavior, and vice versa. Executing a replication study yielded comparable results to our original study, thereby enhancing the validity of our findings.

Culture and Patient-Provider Trust Among People with HIV: Psychometric Properties of the HCR Trust Scale-Revised in Haitian Creole and Spanish.

Patient-provider trust is associated with optimal HIV care engagement and ART adherence. However, cross-cultural examination of trust in providers in this diverse population is limited. The current study examines data from 1272 Miami-Dade County Ryan White Program clients. Study objectives were to explore the psychometric properties of the HCR Trust Scale-Revised translated inHaitian Creole and Spanishand to evaluate similarities and differences in responses to the HCR Trust Scale-Revised across Non-Hispanic Black, Haitian, and Hispanic participants and the English, Haitian Creole, and Spanishtranslations of the scale. Results indicated that the HCR Trust Scale-Revised has a single structure with good reliability for all groups except Haitian participants, for which response patterns were characterized by low variance and high endorsement of alternative response options. We draw on previous literature examining patient-provider experiences to discuss the importance of considering culture in the measurement of patient-provider trust.

Towards a unified measure of general interpersonal trust

Towards a unified measure of general interpersonal trust

A Trust-Based Framework for Mitigating Poisoning Attacks in Federated Learning

Federated Learning (FL) is a decentralized machine learning approach that allows collaborative model training across multiple devices while preserving data privacy. However, its decentralized nature exposes it to poisoning attacks, where malicious clients corrupt model updates, compromising the global model’s integrity. This study introduces a trust-based framework that combines Krum, Differential Privacy (DP), and adaptive trust measures to counter these threats effectively. The framework dynamically assigns trust scores to clients based on their behavior, mitigating the impact of unreliable or malicious updates. Using the MNIST dataset, the framework’s robustness is tested against label flipping and gradient manipulation attacks at varying intensities. Results demonstrate the hybrid approach’s superior performance in accuracy, precision, recall, and F1-scores compared to standalone defenses, showcasing its adaptability and resilience. This research underscores the impor- tance of integrating robust, scalable defense mechanisms in FL to ensure secure, reliable, and trustworthy systems in adversarial settings

Towards the measurement of consumer trust in media brands—scale development and validation

ABSTRACT Media brands in today’s digitized economy are facing growing competition and the increasing need for valid parameters to measure success and the perception of their brand. Trust in media brands is one of the most important factors in that regard, influencing the decision for and against certain media brands and driving factors, such as brand equity and usage intention. While research on trust in corporate, product and service brands is abundant, trust research specifically focusing on media brands has been widely neglected. Due to the strong possible impact of singular events particularly on trust in media brands and the connected high variance in consumer trust, a valid tool to track consumer perceptions over time thus would add a worthwhile contribution to media management. Due to significant differences between brands and media brands based on divergent business models, business partners and sources of revenue, currently available brand trust scales, however, cannot simply be applied in the media sector. In our study, we are conducting quantitative, multinational research to establish a valid and reliable media brand trust scale. We aim for the empirically based development of the focal construct building on the framework of media brand trust. The framework results from 11 components derived from previous studies. With this development, we are providing actors of the media economy with a scale to measure consumers’ trust in the media brand and generating an understanding of the specific elements required to improve media brands’ business operations.

Evaluating cancer patients' experiences with doctor-patient communication in Taiwan: development and validation of a new assessment instrument.

Effective communication between doctors and patients is crucial for the well-being of individuals diagnosed with cancer. This study aimed to develop and validate a cancer-specific Doctor-Patient Communication Satisfaction Scale (DPCSS-Cancer) from the patients' perspective. Initial items were generated through literature review. Content validity was assessed via feedback from five experts, while face validity was determined through cognitive interviews with 13 patients. The revised DPCSS-Cancer was subsequently tested in a sample of 200 cancer patients to assess test-retest stability, internal consistency, factor structure, and criterion-related validity. The development process resulted in a 15-item DPCSS-Cancer across two dimensions, using a 4-point rating scale (1 = strongly disagree to 4 = strongly agree). The item-level Content Validity Index (I-CVI) ranged from 0.8 to 1.0, with a scale-level CVI of 0.96. Following expert and patient feedback, no items were eliminated, but modifications were made to item wording. In formal testing, the overall Cronbach's alpha was 0.96, with 0.89 for the first dimension and 0.95 for the second. Test-retest reliability was established at 0.82. The two-dimensional structure was partially confirmed. Criterion-related validity was supported through a significant positive correlation with a measure of trust in the physician (r = 0.86). Factors contributing to higher DPCSS-Cancer scores were identified. The DPCSS-Cancer shows satisfactory reliability and validity, making it a viable patient-reported outcome measure for assessing cancer patients' satisfaction with doctor-patient communication.

Access to affordable daycare and women's mental health in Rajasthan, India: Evidence from a cluster-randomised social intervention.

Women in India are often responsible for unpaid household work, family caregiving, and paid work, which can contribute to poorer mental health. The provision of childcare has the potential to improve women's mental health, but evidence on the effects of providing access to daycare is limited. We designed a cluster-randomised trial and used data from a sample of 2858 mothers with age-eligible children from 160 village hamlets in rural Rajasthan, India, to evaluate the impact of providing access to a community-based daycare programme on social and emotional aspects of women's mental health. We conducted a baseline survey in early 2016, randomised hamlets to intervention or control groups approximately six months later, and delivered the final post-intervention survey approximately two years thereafter. Treatment assignment increased the probability that a respondent used a daycare over the two-year follow-up by 40.9 percentage points. Providing randomised access to a daycare resulted in 0.2 (95% confidence interval (CI) = -0.1, 0.4) fewer symptoms of mental distress, representing a 9.5% decline compared to the baseline mean of 2.1 symptoms, as well as a 3.7 (95% CI = -0.8, 8.3) percentage point increase in the proportion of women who reported feeling very happy, equivalent to an 11.0% increase relative to the baseline mean of 33.6%. Among social indicators, treatment assignment was associated with a 5.6 (95% CI = -1.2, 12.4) percentage point increase in membership in an association, a relative increase of 43.4% compared to the baseline mean of 12.9%. The intervention did not have an appreciable impact on measures of life satisfaction or trust in institutions. Two-stage least squares instrumental variable analyses showed that daycare use decreased mental distress by 0.4 (95% CI = -0.1, 0.8) symptoms, increased the proportion of women who were very happy by 9.4 (95% CI = 0.0, 17.6) percentage points, and increased membership in an organisation by 15.9 (95% CI = 8.4, 23.7) percentage points. The provision of affordable, community-based daycare was associated with substantial uptake and showed potential for improving mothers' mental health in a rural context where most women were not employed in the formal labour force. ISRCTN clinical trial registry (ISRCTN45369145), registered on 16 May 2016; American Economic Association's registry for randomised controlled trials (AEARCTR-0000774), registered on 15 July 2015.

Too much of a good thing: How varying levels of automation impact user performance in a simulated intrusion detection task

Cyber analysts face a demanding task when prioritizing alerts from intrusion detection systems, balancing the challenge of numerous false positives from rule-based methods with the critical need to detect genuine cyber threats, necessitating unwavering vigilance and imposing a significant cognitive burden. In this field, there exists pressure to incorporate artificial intelligence techniques to enhance the automation of analyst workflows, yet without a clear grasp of how elevating the Level of Automation impacts the allocation of attentional and cognitive resources among analysts. This paper describes a simulated AI-assisted intrusion detection task which varies five degrees of automation as well as the sensitivity of the assistant, evaluating performance-based (e.g., accuracy, response time, sensitivity, response bias) and subjective (e.g., surveys on workload and trust) measures. Participants white-listed a series of time-sensitive alerts in a simulated Snort® environment. Our findings indicate that elevating the level of automation altered participants’ behavior, evident in their tendency to display a response bias towards rejecting hits (reduced hit rate and false alarm rate) when overriding an AI’s decision. Additionally, participants subjectively reported experiencing a decreased cognitive workload with a more precise algorithm, irrespective of any variance in their actual performance. Our findings suggest the necessity for additional research before implementing further automation into analyst workflows, as the demands of tasks evolve with escalating levels of automation.

How Explainable Artificial Intelligence Can Increase or Decrease Clinicians' Trust in AI Applications in Health Care: Systematic Review.

Artificial intelligence (AI) has significant potential in clinical practice. However, its "black box" nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians' trust in AI-driven clinical decision-making. A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians' trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians' trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians' trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice.

Balancing Human Likeness in Social Robots: Impact on Children’s Lexical Alignment and Self-disclosure for Trust Assessment

While there is evidence that human-like characteristics in robots could benefit child-robot interaction in many ways, open questions remain about the appropriate degree of human likeness that should be implemented in robots to avoid adverse effects on acceptance and trust. This study investigates how human likeness, appearance and behavior, influence children’s social and competency trust in a robot. We first designed two versions of the Furhat robot with visual and auditory human-like and machine-like cues validated in two online studies. Secondly, we created verbal behaviors where human likeness was manipulated as responsiveness regarding the robot’s lexical matching. Then, 52 children (7–10 years old) played a storytelling game in a between-subjects experimental design. Results show that the conditions did not affect subjective trust measures. However, objective measures showed that human likeness affects trust differently. While low human-like appearance enhanced social trust, high human-like behavior improved children’s acceptance of the robot’s task-related suggestions. This work provides empirical evidence on manipulating facial features and behavior to control human likeness in a robot with a highly human-like morphology. We discuss the implications and importance of balancing human likeness in robot design and its impacts on task performance, as it directly impacts trust-building with children.

ChatGPT Virtual Assistant for Breast Reconstruction: Assessing Preferences for a Traditional Chatbot versus a Human AI VideoBot.

Recent advancements in artificial intelligence (AI) have reshaped telehealth, with AI chatbots like Chat Generative Pretrained Transformer (ChatGPT) showing promise in various medical applications. ChatGPT is capable of offering basic patient education on procedures in plastic and reconstructive surgery (PRS), yet the preference between human AI VideoBots and traditional chatbots in plastic and reconstructive surgery remains unexplored. We developed a VideoBot by integrating ChatGPT with Synthesia, a human AI avatar video platform. The VideoBot was then integrated into Tolstoy to create an interactive experience that answered four of the most asked questions related to breast reconstruction. We used Zapier to develop a ChatGPT-integrated chatbot. A 16-item survey adapted from the 2005 validated measurement of online trust by Corritore et al was distributed online to female participants via Amazon Mechanical Turk. A total of 396 responses were gathered. Participants were 18 to 64 years old. Perceptions of truthfulness, believability, content expertise, ease of use, and safety were similar between the VideoBot and chatbot. Most participants preferred the VideoBot compared with the traditional chatbot (63.5% versus 28.1%), as they found it more captivating than the text-based chatbot. Of the participants, 77% would have preferred to see someone who they identified with in terms of gender and race. Both the VideoBot and text-based chatbot show comparable effectiveness, usability, and trust. Nonetheless, the VideoBot's human-like qualities enhance interactivity. Future research should explore the impact of race and gender concordance in telehealth to provide a more personalized experience for patients.

Effects of Cognition-based and Affect-based Trust Attitudes on Trust Intentions

Despite decades of research on trust in the workplace, researchers continue to struggle with fundamental questions regarding the conceptualization and measurement of organizational trust. To help clarify this construct, we revisit established trust definitions (Mayer et al. Academy of Management Review, 20(3), 709–734, Mayer et al., Academy of Management Review 20:709–734, 1995; Rousseau et al. Academy of Management Review, 23(3), 393–404, Rousseau et al., Academy of Management Review 23:393–404, 1998) and distinguish trust attitudes (i.e., positive expectations of others) from trust intentions (i.e., the willingness to be vulnerable). Using a three-study experimental design, we examined the causal effect of two distinct trust attitudes (i.e., cognition-based and affect-based) on two trust intentions (i.e., reliance and disclosure). We found that when cognition-based trust was high, participants were more willing to rely on a colleague (i.e., had higher reliance intentions). When affect-based trust was high, participants were more willing to share sensitive information with that colleague (i.e., higher disclosure intentions) and more willing to rely on a colleague (i.e., higher reliance intentions). We also examined the effect of mixed trust attitudes (i.e., feelings of low (vs. high) cognition-based trust paired with high (vs. low) affect-based trust). We found that, for reliance intentions, for the most part, high affect-based trust could be substituted with high cognition-based trust. Conversely, for disclosure intentions, high cognition-based trust could not substitute for high affect-based trust. The observed patterns indicate that affect-based and cognition-based attitudes are related yet distinct, with differential patterns of prediction with reliance and disclosure intentions. Our findings also underscore the importance of affect-based trust. By nurturing strong interpersonal bonds among employees, organizations can improve communication and collaboration, critical elements for organizational effectiveness.

The drunk side of trust: Generalized and instantaneous trust at gathering events

We present novel evidence on the nexus between alcohol intake and trust at a large cultural gathering event. Throughout six editions, we interviewed nearly 2,000 attendees and collected an objective measure of alcohol intake (Blood Alcohol Concentration, BAC) using electronic breathalyzers and self-perceived measures of intoxication. We elicited different self-reported trust measures toward eventgoers and the general public. While alcohol intoxication is not correlated with trust toward the general public, there is a positive and significant nexus between alcohol intake and trust toward the other participants in the event (Instantaneous trust). Only a small part of this effect (15%) is driven by an increased trust in other drinkers at the event. Taken together, these findings indicate endogenous group formation regarding alcohol intake.

Investing in the future: an integrated model for analysing user attitudes towards Robo-advisory services with AI integration

Purpose Recognizing the importance of Robo-advisors in digital financial services, this paper aims to analyse the users’ perception and acceptability of artificial intelligence (AI) in digital investment solutions using an extended “Technology Acceptance Model” (TAM). Design/methodology/approach The model is tested using 454 online valid responses received from Indian Fintech users via direct path analysis, mediation and moderation. Findings The study’s findings show that trust, perceived usefulness and perceived risk all significantly impact users’ attitudes towards Robo-advisors. In contrast, ease of use and social influence did not impact users’ attitudes statistically. Furthermore, the results indicate that their attitudes and ease of use influence users’ intentions to adopt Robo-advisors. Moreover, the moderation effect of gender partly supports the overall model. Specifically, in the path between attitudes and their antecedents, gender plays a role in influencing the relationships among these variables. This aligns with preliminary research in the field, providing additional insight into how gender may moderate the factors influencing users’ attitudes and intentions regarding Robo-advisory services. Research limitations/implications This research study also reveals that trust, perceived risk, ease of use and demographic factors influence the adoption of Robo-advisory services. It is functional, but its sample selection is not probabilistic and overly emphasizes gender. Future research should use probabilistic sampling, other demographic factors and experience and situational factors. Also, it is necessary to examine how convenient and satisfying it is to communicate with service providers. Filling these gaps will improve the knowledge of consumer behaviour in the context of Fintech adoption and develop the current research. Practical implications This study posits that perceived usefulness, trust, perceived risk and ease of use remain core determinants of adopting Robo-advisory services. So, to improve the level of trust of users, it is necessary to develop security measures, data clarity and quality and customer support. Enhancing ease of use by incorporating better interface gestures is always beneficial for increasing the number of users and their level of satisfaction. As identified in previous studies, practical solutions will be achieved by pursuing the increased use of technology while leveraging AI for personal services and minimizing perceived risks, which will strengthen more advanced security measures as well as sufficiently clear communication. Originality/value The paper aims to extend the TAM by incorporating measures of trust and social influence to identify the factors that drive the adoption of Robo-advisors. In doing so, the paper may contribute to developing a more comprehensive understanding of the factors that shape consumers’ attitudes and intentions towards these technologies. Moreover, the paper appears to examine the moderating effect of gender on attitude and its predictors, which could provide insights into how gender characteristics may impact the adoption of Robo-advisors.

Rely on whom you trust? Trust and financial planning for retirement

We study the role of trust in shaping financial planning for retirement under a three-pillar pension system. Using data from two waves of the China Retirement Readiness Index Survey and multiple measures of trust in various entities, we find that trust in government, employers, and financial institutions increases the probability of developing a financial plan involving government, employer, and self-provision, respectively, as well as the estimated proportions of retirement income derived from each source. Our study deepens the understanding of how specific trust affects planning practices in the context of multiple retirement income sources and a culture featuring filial piety.

Manager’s Trust and Trustworthiness in Sustainable Practices: Impact on Turnover and Manager’s Performance in Restaurants in China

This study examines the effects of managers’ trusting and trustworthy behaviors toward employees on turnover rates and performance evaluations within 115 restaurants in China. Utilizing data from a large-scale hypothetical experiment involving over 2000 employees and managers, combined with performance and operational data from these restaurants, we applied both game theoretical and empirical methods to analyze the dataset. Our findings reveal that managers’ trusting and trustworthy actions are significantly associated with lower turnover rates, highlighting how trust-driven leadership can enhance organizational stability and sustainable business practices. For senior associate managers, their trusting actions toward employees positively impact their performance. Furthermore, senior associate managers working in restaurants where employees are more trusting and trustworthy toward their peers tend to receive higher performance ratings. This study uniquely connects behavioral trust and trustworthiness measures with objective performance outcomes, offering new insights into the critical role of trust in fostering sustainable business practices and improving economic performance.

Fact-checker warning labels are effective even for those who distrust fact-checkers.

Warning labels from professional fact-checkers are one of the most widely used interventions against online misinformation. But are fact-checker warning labels effective for those who distrust fact-checkers? Here, in a first correlational study (N = 1,000), we validate a measure of trust in fact-checkers. Next, we conduct meta-analyses across 21 experiments (total N = 14,133) in which participants evaluated true and false news posts and were randomized to either see no warning labels or to see warning labels on a high proportion of the false posts. Warning labels were on average effective at reducing belief in (27.6% reduction), and sharing of (24.7% reduction), false headlines. While warning effects were smaller for participants with less trust in fact-checkers, warning labels nonetheless significantly reduced belief in (12.9% reduction), and sharing of (16.7% reduction), false news even for those most distrusting of fact-checkers. These results suggest that fact-checker warning labels are a broadly effective tool for combatting misinformation.

Do Generative Artificial Intelligence Company Strategies of ‘Moving Fast and Breaking Things’ in Civil Society Cancel Their Social Licence to Operate? A Nurse’s Evaluation of Chatbot Impacts

A rapid expansion of the computer technology industry, particularly in the field of artificial intelligence, has ignited a global concern that warrants our immediate action. As nurses, our professional values frameworks compel us to protect public health and address national and global health issues. When industry activities adversely affect the social wellbeing of civil society and social institutions, it is important to evaluate them against their industry’s ‘social license to operate, which is a measure of public trust, credibility, and the legitimacy of their industrial and corporate citizenship status. The central question is, do computer technology companies continue to have a social license to operate in civil society? Nurses are encouraged to evaluate the computer technology industry’s recent ‘generative artificial intelligence’ chatbot activities against its tacit undertaking to be good corporate citizens in return for social acceptance of their operations and behaviour. An evidence-based overview of chatbot impacts on societies, environmental sustainability and human rights provide a basis for evaluation. Basic computer technology terminology and relevant concepts are explained. This article is a direct call to action for clinical nurses and those involved in research, education, management, and policy. We have a duty to critically assess the claims made by chatbot technology vendors in both practice and social contexts. If these vendors integrate chatbot technologies with existing machine learning used in nursing and healthcare technologies it could result in detrimental effects beyond user control. By influencing decisions on technology adoption, we can ensure the implementation of safeguards, protect patient safety and social well-being, and uphold the integrity of nursing values. A closing discussion of impacts of computer industry trust deficits on healthcare and research reflects the author’s concerns and conclusions about the central question. Readers may draw other conclusions and perhaps use the issues and evidence presented here to stimulate further investigations.

Self-rated health of Latino day laborers during the COVID-19 pandemic: a cross-sectional study.

This study assesses the relationship between trust in sources of information in regard to the Coronavirus Disease 2019 (COVID-19) pandemic and self-rated health (SRH) in a sample of Latino Day Laborers (LDLs) and explores whether these associations were mediated by mental health measures. A rapid needs assessment survey was conducted with 300 LDLs, recruited at randomly selected hiring locations in Houston, Texas, during November and December 2021. Two measures of trust were developed, and SRH was measured by a single item. Depression, anxiety, and stress were measured by previously validated scales. We applied the product of coefficients approach to assess our mediation model. Using Hayes' PROCESS version 4.2 in SPSS, linear regression models were generated simultaneously to assess the total effect of trust on SRH, effect of trust on the mental health mediator, and effect of the mediator on SRH, controlling for trust. Demographic characteristics were entered as covariates. Greater trust in formal sources of information (such as Spanish-speaking television networks) as well as greater levels of anxiety and depression were associated with lower SRH. There were no significant associations between formal trust and mental health measures. As such, the association between formal trust and decreased SRH was not mediated by mental health. Trust in informal sources of information (conversations with friends, family, and coworkers) was not significantly associated with SRH or mental health. Higher levels of depression and anxiety, however, were associated with lower SRH. LDLs' perception of their health was inversely associated with their level of trust in formal sources of information and with greater levels of depression and anxiety. Nevertheless, these pathways were independent of each other. The results indicate the direct impact of COVID-19 public information on subjective well-being, a relationship that merits further exploration.

Effect of Shared Decision-Making on Trust in Physicians in the Management of Systemic Lupus Erythematosus: The Trust Measurement for Physicians and Patients With Systemic Lupus Erythematosus Prospective Cohort Study.

Few studies have explored whether the involvement of patients in shared decision-making (SDM) is beneficial to the management of systemic lupus erythematosus (SLE). Therefore, this study investigated the relationship between patient participation in SDM and their trust in physicians using data from the Trust Measurement in Physicians and Patients With SLE (TRUMP2-SLE) study. Data regarding the nine-item Japanese version of the Shared Decision-Making Questionnaire (SDM-Q-9) scores, Trust in Physician Scale (TIPS) scores, and Abbreviated Wake Forest Physician Trust Scale (A-WFPTS) scores for interpersonal trust in a physician and trust in the medical profession were collected from patients with SLE who visited the outpatient clinics of five facilities in Japan through a self-administered questionnaire. The relationships among these scores were analyzed by general linear models with cluster-robust variance. This study included 433 patients with SLE. The median baseline TIPS and A-WFPTS (attending physician version) scores were 82 (73-93) and 80 (70-95), respectively. A higher baseline SDM-Q-9 score was correlated with an increase in the TIPS score at one year (coefficient per 10-point [pt] increase, 0.94 pts, 95% confidence interval [CI] 0.16-1.72). A higher baseline SDM-Q-9 score was correlated with a higher A-WFPTS score for interpersonal trust (coefficient per 10-pt increase, 2.20 pts, 95% CI 1.44-2.96). The baseline SDM-Q-9 score was also correlated with an increase in the general physician version of the A-WFPTS score at one year (coefficient per 10-pt increase, 1.29 pts, 95% CI 0.41-2.18). Engagement of patients with SLE in SDM elevates their trust in the attending physicians and health care providers, potentially enhancing doctor-patient relationships and overall health care trust.