Updating beliefs about pain following advice: Trustworthiness of social advice predicts pain expectations and experience

Prior expectations influence pain experience. These expectations, in turn, rely on prior pain experience, but they may also be socially influenced. Yet, most research has focused on self rather than social expectations about pain, and hardly any studies examined their combined effects on pain. Here, we adopted a Bayesian learning perspective to investigate how explicitly communicated social expectations ('advice about pain tolerance') affect own pain expectations, and ultimately pain tolerance, under varying conditions of social epistemic uncertainty (trustworthiness of the advice). N = 72 female participants took part in a coldpressor (cold water) task before (self-learning baseline) and after (socially-influenced learning) receiving advice about their likely pain tolerance from a confederate, the trustworthiness of whom was experimentally manipulated. We used path analysis to test the hypothesis that social advice from a highly trustworthy confederate would influence participants' expectations about pain more than advice from a less trustworthy source, and that the degree of this social influence would in turn predict pain tolerance. We further used a simplified, Bayesian learning, computational approach for explicit belief updating to examine the role of latent parameters of precision optimisation in how participants subsequently changed their future pain expectations (prospective posterior beliefs) based on the combined effect of the confederate's advice on their own pain expectations, and their own task experience. Results confirmed that participants adjusted their pain expectations towards the confederate's advice more in the high- vs. low-trustworthiness condition, and this advice taking predicted their pain tolerance. Furthermore, the confederate's trustworthiness influenced how participants weighted the confederate's advice in relation to their own expectations and task experience in forming prospective posterior beliefs. When participants received advice from a less trustworthy confederate, their own sensory experience was weighted more highly than their socially-influenced prior expectations. Thus, explicit social advice appears to impact pain by influencing one's own pain expectations, but low social trustworthiness leads to these expectations becoming more malleable to novel, sensory learning.

Privacy preserving image registration

Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we propose several techniques to optimize the image registration operations by using gradient approximations, and by revisiting the use of homomorphic encryption trough packing, to allow the efficient encryption and multiplication of large matrices. We focus on registration methods of increasing complexity, including rigid, affine, and non-linear registration based on cubic splines or diffeomorphisms parameterized by time-varying velocity fields. In all these settings, we demonstrate how the registration problem can be naturally adapted for accounting to privacy-preserving operations, and illustrate the effectiveness of PPIR on a variety of registration tasks.

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation

BACKGROUND AND OBJECTIVE

The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular form of the esophagus and small size, the inconsistency of spatio-temporal structure, and low contrast of esophagus and its peripheral tissues in medical images. The objective of this study is to improve the segmentation effect of esophageal squamous cell carcinoma lesions.

METHODS

It is critical for a segmentation network to effectively extract 3D discriminative features to distinguish esophageal cancers from some visually closed adjacent esophageal tissues and organs. In this work, an efficient HRU-Net architecture (High-Resolution U-Net) was exploited for esophageal cancer and esophageal carcinoma segmentation in CT slices. Based on the idea of localization first and segmentation later, the HRU-Net locates the esophageal region before segmentation. In addition, an Resolution Fusion Module (RFM) was designed to integrate the information of adjacent resolution feature maps to obtain strong semantic information, as well as preserve the high-resolution features.

RESULTS

Compared with the other five typical methods, the devised HRU-Net is capable of generating superior segmentation results.

CONCLUSIONS

Our proposed HRU-NET improves the accuracy of segmentation for squamous esophageal cancer. Compared to other models, our model performs the best. The designed method may improve the efficiency of clinical diagnosis of esophageal squamous cell carcinoma lesions.

Alcohol does not influence trust in others or oxytocin, but increases positive affect and risk-taking: a randomized, controlled, within-subject trial

BACKGROUND

Alcohol consumption to facilitate social interaction is an important drinking motive. Here, we tested whether alcohol influences trust in others via modulation of oxytocin and/or androgens. We also aimed at confirming previously shown alcohol effects on positive affect and risk-taking, because of their role in facilitating social interaction.

METHODS

This randomized, controlled, within-subject, parallel group, alcohol-challenge experiment investigated the effects of alcohol (versus water, both mixed with orange juice) on perceived trustworthiness via salivary oxytocin (primary and secondary endpoint) as well as testosterone, dihydrotestosterone, positive affect, and risk-taking (additional endpoints). We compared 56 male participants in the alcohol condition (1.07 ± 0.18 per mille blood alcohol concentration) with 20 in the control condition.

RESULTS

The group (alcohol versus control condition) × time (before [versus during] versus after drinking) interactions were not significantly associated with perceived trustworthiness (η2 < 0.001) or oxytocin (η2 = 0.003). Bayes factors provided also substantial evidence for the absence of these effects (BF01 = 3.65; BF01 = 7.53). The group × time interactions were related to dihydrotestosterone (η2 = 0.018 with an increase in the control condition) as well as positive affect and risk-taking (η2 = 0.027 and 0.007 with increases in the alcohol condition), but not significantly to testosterone.

DISCUSSION

The results do not verify alcohol effects on perceived trustworthiness or oxytocin in male individuals. However, they indicate that alcohol (versus control) might inhibit an increase in dihydrotestosterone and confirm that alcohol amplifies positive affect and risk-taking. This provides novel mechanistic insight into social facilitation as an alcohol-drinking motive.

Trials We Cannot Trust: Investigating Their Impact on Systematic Reviews and Clinical Guidelines in Spinal Pain

We previously conducted an exploration of the trustworthiness of a group of clinical trials of cognitive-behavioral therapy and exercise in spinal pain. We identified multiple concerns in 8 trials, judging them untrustworthy. In this study, we systematically explored the impact of these trials ("index trials") on results, conclusions, and recommendations of systematic reviews and clinical practice guidelines (CPGs). We conducted forward citation tracking using Google Scholar and the citationchaser tool, searched the Guidelines International Network library and National Institute of Health and Care Excellence archive to June 2022 to identify systematic reviews and CPGs. We explored how index trials impacted their findings. Where reviews presented meta-analyses, we extracted or conducted sensitivity analyses for the outcomes of pain and disability, to explore how the exclusion of index trials affected effect estimates. We developed and applied an 'Impact Index' to categorize the extent to which index studies impacted their results. We included 32 unique reviews and 10 CPGs. None directly raised concerns regarding the veracity of the trials. Across meta-analyses (55 comparisons), the removal of index trials reduced effect sizes by a median of 58% (Inter Quartlie Range (IQR) 40-74). 85% of comparisons were classified as highly, 3% as moderately, and 11% as minimally impacted. Nine out of 10 reviews conducting narrative synthesis drew positive conclusions regarding the intervention tested. Nine out of 10 CPGs made positive recommendations for the intervention(s) evaluated. This cohort of trials, with concerns regarding trustworthiness, has substantially impacted the results of systematic reviews and guideline recommendations. PERSPECTIVE: We found that a group of trials of CBT for spinal pain with concerns relating to their trustworthiness has had substantial impacts on the analyses and conclusions of systematic reviews and clinical practice guidelines. This highlights the need for a greater focus on the trustworthiness of studies in evidence appraisal. PRE-REGISTRATION: Our protocol was preregistered on the Open Science Framework: https://osf.io/m92ax/.

Artificial intelligence and clinical decision support: clinicians' perspectives on trust, trustworthiness, and liability

Artificial intelligence (AI) could revolutionise health care, potentially improving clinician decision making and patient safety, and reducing the impact of workforce shortages. However, policymakers and regulators have concerns over whether AI and clinical decision support systems (CDSSs) are trusted by stakeholders, and indeed whether they are worthy of trust. Yet, what is meant by trust and trustworthiness is often implicit, and it may not be clear who or what is being trusted. We address these lacunae, focusing largely on the perspective(s) of clinicians on trust and trustworthiness in AI and CDSSs. Empirical studies suggest that clinicians' concerns about their use include the accuracy of advice given and potential legal liability if harm to a patient occurs. Onora O'Neill's conceptualisation of trust and trustworthiness provides the framework for our analysis, generating a productive understanding of clinicians' reported trust issues. Through unpacking these concepts, we gain greater clarity over the meaning ascribed to them by stakeholders; delimit the extent to which stakeholders are talking at cross purposes; and promote the continued utility of trust and trustworthiness as useful concepts in current debates around the use of AI and CDSSs.

Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis

BACKGROUND

When the COVID-19 pandemic commenced in 2020, scientists assisted medical specialists with diagnostic algorithm development. One scientific research area related to COVID-19 diagnosis was medical imaging and its potential to support molecular tests. Unfortunately, several systems reported high accuracy in development but did not fare well in clinical application. The reason was poor generalization, a long-standing issue in AI development. Researchers found many causes of this issue and decided to refer to them as confounders, meaning a set of artefacts and methodological errors associated with the method. We aim to contribute to this steed by highlighting an undiscussed confounder related to image resolution.

METHODS

20 216 chest X-ray images (CXR) from worldwide centres were analyzed. The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. Additional 44 339 thorax CXRs were used for validation. The comprehensive analysis of the multimodality of the density distribution in rUMAP/nUMAP domains and its relation to the original image properties was used to identify the main confounders.

RESULTS

nUMAP revealed a hidden bias of neural networks towards the image resolution, which the regular up-sampling procedure cannot compensate for. The issue appears regardless of the network architecture and is not observed in a high-resolution dataset. The impact of the resolution heterogeneity can be partially diminished by applying advanced deep-learning-based super-resolution networks.

CONCLUSIONS

rUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision.

Three's a crowd: Fast ensemble perception of first impressions of trustworthiness

Trustworthiness impressions are fundamental social judgements with far-reaching consequences in many aspects of society, including criminal justice, leadership selection and partner preferences. Thus far, most research has focused on facial characteristics that make a face individually appear more or less trustworthy. However, in everyday life, faces are not always perceived in isolation but are often encountered in crowds. It has been proposed that we deal with the large amount of facial information in a group by extracting summary statistics of the crowd, a phenomenon called ensemble perception. Prior research showed that ensemble perception occurs for various facial features, such as emotional expression, facial identity, and attractiveness. Here, we investigated whether observers can integrate the level of trustworthiness from multiple faces to extract an average impression of the crowd. Across four studies, participants were presented with crowds of faces and were asked to report their average level of trustworthiness with an adjustment (Experiment 1) and a rating task (Experiments 2 and 3). Participants were able to extract an ensemble perception of trustworthiness impressions from multiple faces. Moreover, observers were able to form a summary statistic of trustworthiness impressions from a group of faces as quickly as 250 ms (Experiment 4). Taken together, these results demonstrate that ensemble perception can occur at the level of impressions of trustworthiness. Thus, these critical social judgements not only occur for individual faces but are also integrated into a unique ensemble impression of crowds. Our findings contribute to the development of a more ecological approach to the study of trust impressions, since they provide an understanding of trustworthiness judgements not only on an individual level, but on a much broader social group level. Furthermore, our results drive forward new theory because they demonstrate for the first time that ensemble representations cover a broad range of phenomena than previously recognized, including complex high-level facial trait judgements such as trustworthiness impressions.

Patient priorities for fulfilling the principle of respect in research: findings from a modified Delphi study

BACKGROUND

Standard interpretations of the ethical principle of respect for persons have not incorporated the views and values of patients, especially patients from groups underrepresented in research. This limits the ability of research ethics scholarship, guidance, and oversight to support inclusive, patient-centered research. This study aimed to identify the practical approaches that patients in community-based settings value most for conveying respect in genomics research.

METHODS

We conducted a 3-round, web-based survey using the modified Delphi technique to identify areas of agreement among English-speaking patients at primary care clinics in Washington State and Idaho who had a personal or family history of cancer. In Round 1, respondents rated the importance of 17 items, identified in prior qualitative work, for feeling respected. In Round 2, respondents re-rated each item after reviewing overall group ratings. In Round 3, respondents ranked a subset of the 8 most highly rated items. We calculated each item's mean and median rankings in Round 3 to identify which approaches were most important for feeling respected in research.

RESULTS

Forty-one patients consented to the survey, 21 (51%) completed Round 1, and 18 (86% of Round 1) completed each of Rounds 2 and 3. Two sets of rankings were excluded from analysis as speed of response suggested they had not completed the Round 3 ranking task. Respondents prioritized provision of study information to support decision-making (mean ranking 2.6 out of 8; median ranking 1.5) and interactions with research staff characterized by kindness, patience, and a lack of judgment (mean ranking 2.8; median ranking 2) as the most important approaches for conveying respect.

CONCLUSIONS

Informed consent and interpersonal interactions are key ways that research participants experience respect. These can be supported by other approaches to respecting participants, especially when consent and/or direct interactions are infeasible. Future work should continue to engage with patients in community-based settings to identify best practices for research without consent and examine unique perspectives across clinical and demographic groups in different types of research.

Trustworthy Medical Image Segmentation with improved performance for in-distribution samples

Despite the enormous achievements of Deep Learning (DL) based models, their non-transparent nature led to restricted applicability and distrusted predictions. Such predictions emerge from erroneous In-Distribution (ID) and Out-Of-Distribution (OOD) samples, which results in disastrous effects in the medical domain, specifically in Medical Image Segmentation (MIS). To mitigate such effects, several existing works accomplish OOD sample detection; however, the trustworthiness issues from ID samples still require thorough investigation. To this end, a novel method TrustMIS (Trustworthy Medical Image Segmentation) is proposed in this paper, which provides the trustworthiness and improved performance of ID samples for DL-based MIS models. TrustMIS works in three folds: IT (Investigating Trustworthiness), INT (Improving Non-Trustworthy prediction) and CSO (Classifier Switching Operation). Initially, the IT method investigates the trustworthiness of MIS by leveraging similar characteristics and consistency analysis of input and its variants. Subsequently, the INT method employs the IT method to improve the performance of the MIS model. It leverages the observation that an input providing erroneous segmentation can provide correct segmentation with rotated input. Eventually, the CSO method employs the INT method to scrutinise several MIS models and selects the model that delivers the most trustworthy prediction. The experiments conducted on publicly available datasets using well-known MIS models reveal that TrustMIS has successfully provided a trustworthiness measure, outperformed the existing methods, and improved the performance of state-of-the-art MIS models. Our implementation is available at https://github.com/SnehaShukla937/TrustMIS.

Building trust: Leadership reflections on community empowerment and engagement in a large urban initiative

BACKGROUND

Trust is essential for healthy, reciprocal relationships; creating safe environments; engaging in transparent interactions; successfully negotiating power differentials; supporting equity and putting trauma informed approaches into practice. Less is known, however, about the ways that trust-building may be at the forefront of consideration during community capacity building efforts, what trust-building elements are perceived as essential for optimally engaging communities, and what practices might support these efforts.

METHODS

The present study examines an evolving understanding of trust-building over the course of 3 years, from qualitative data derived during interviews with nine agency leads from a large and diverse urban community, who are spearheading community-based partnerships to create more trauma-informed communities and foster resiliency.

RESULTS

Data reflected fourteen trust-building elements, captured by three themes: 1) Building relationships and engagement (e.g., behavioral practices such as meeting people "where they are at" and creating safe spaces), 2) Embodying core values of trustworthiness (e.g., traits such as being transparent and embodying benevolence), and 3) Sharing decision-making, championing autonomy, and addressing barriers to trust (e.g., collaborative practices such as creating a shared vision and goals and addressing systemic inequities). These trust-building elements are presented in the Community Circle of Trust-Building, which provides an accessible, visual format that can facilitate capacity building efforts within organizations and with the broader community; guide the selection of training opportunities that support healthy interpersonal relationships; and aid in the identification of relevant, supporting frameworks (e.g., health equity, trauma-informed practices, inclusive leadership models).

CONCLUSIONS

Community engagement and trust are essential for overall health and well-being, increasing equitable access to resources, and supporting an effective and connected citizenry. These data shed light on opportunities for trust-building and thoughtful engagement among agencies working directly with community members in large urban areas.

Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care

BACKGROUND

Despite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.

METHODS

Seventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.

RESULTS

Three themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.

CONCLUSIONS

The ethical values that underscore birth parents and mothers' perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.

Trustworthy management in hospital settings: a systematic review

BACKGROUND

Trustful relationships play a vital role in successful organisations and well-functioning hospitals. While the trust relationship between patients and providers has been widely studied, trust relations between healthcare professionals and their supervisors have not been emphasised. A systematic literature review was conducted to map and provide an overview of the characteristics of trustworthy management in a hospital setting.

METHODS

We searched Web of Science, Embase, MEDLINE, APA PsycInfo, CINAHL, Scopus, EconLit, Taylor & Francis Online, SAGE Journals and Springer Link from database inception up until Aug 9, 2021. Empirical studies written in English undertaken in a hospital or similar setting and addressed trust relationships between healthcare professionals and their supervisors were included, without date restrictions. Records were independently screened for eligibility by two researchers. One researcher extracted the data and another one checked the correctness. A narrative approach, which involves textual and tabular summaries of findings, was undertaken in synthesising and analysing the data. Risk of bias was assessed independently by two researchers using two critical appraisal tools. Most of the included studies were assessed as acceptable, with some associated risk of bias.

RESULTS

Of 7414 records identified, 18 were included. 12 were quantitative papers and 6 were qualitative. The findings were conceptualised in two categories that were associated with trust in management, namely leadership behaviours and organisational factors. Most studies (n = 15) explored the former, while the rest (n = 3) additionally explored the latter. Leadership behaviours most commonly associated with employee's trust in their supervisors include (a) different facets of ethical leadership, such as integrity, moral leadership and fairness; (b) caring for employee's well-being conceptualised as benevolence, supportiveness and showing concern and (c) the manager's availability measured as being accessible and approachable. Additionally, four studies found that leaders' competence were related to perceptions of trust. Empowering work environments were most commonly associated with trust in management.

CONCLUSIONS

Ethical leadership, caring for employees' well-being, manager's availability, competence and an empowering work environment are characteristics associated with trustworthy management. Future research could explore the interplay between leadership behaviours and organisational factors in eliciting trust in management.

Checklist to assess Trustworthiness in RAndomised Controlled Trials (TRACT checklist): concept proposal and pilot

OBJECTIVES

To propose a checklist that can be used to assess trustworthiness of randomized controlled trials (RCTs).

DESIGN

A screening tool was developed using the four-stage approach proposed by Moher et al. This included defining the scope, reviewing the evidence base, suggesting a list of items from piloting, and holding a consensus meeting. The initial checklist was set-up by a core group who had been involved in the assessment of problematic RCTs for several years. We piloted this in a consensus panel of several stakeholders, including health professionals, reviewers, journal editors, policymakers, researchers, and evidence-synthesis specialists. Each member was asked to score three articles with the checklist and the results were then discussed in consensus meetings.

OUTCOME

The Trustworthiness in RAndomised Clinical Trials (TRACT) checklist includes 19 items organised into seven domains that are applicable to every RCT: 1) Governance, 2) Author Group, 3) Plausibility of Intervention Usage, 4) Timeframe, 5) Drop-out Rates, 6) Baseline Characteristics, and 7) Outcomes. Each item can be answered as either no concerns, some concerns/no information, or major concerns. If a study is assessed and found to have a majority of items rated at a major concern level, then editors, reviewers or evidence synthesizers should consider a more thorough investigation, including assessment of original individual participant data.

CONCLUSIONS

The TRACT checklist is the first checklist developed specifically to detect trustworthiness issues in RCTs. It might help editors, publishers and researchers to screen for such issues in submitted or published RCTs in a transparent and replicable manner.

COVID-19 vaccination decisions among Gypsy, Roma, and Traveller communities: A qualitative study moving beyond "vaccine hesitancy"

BACKGROUND

Many people refuse vaccination and it is important to understand why. Here we explore the experiences of individuals from Gypsy, Roma, and Traveller groups in England to understand how and why they decided to take up or to avoid COVID-19 vaccinations.

METHODS

We used a participatory, qualitative design, including wide consultations, in-depth interviews with 45 individuals from Gypsy, Roma, and Traveller, communities (32 female, 13 male), dialogue sessions, and observations, in five locations across England between October 2021 and February 2022.

FINDINGS

Vaccination decisions overall were affected by distrust of health services and government, which stemmed from prior discrimination and barriers to healthcare which persisted or worsened during the pandemic. We found the situation was not adequately characterised by the standard concept of "vaccine hesitancy". Most participants had received at least one COVID-19 vaccine dose, usually motivated by concerns for their own and others' health. However, many participants felt coerced into vaccination by medical professionals, employers, and government messaging. Some worried about vaccine safety, for example possible impacts on fertility. Their concerns were inadequately addressed or even dismissed by healthcare staff.

INTERPRETATION

A standard "vaccine hesitancy" model is of limited use in understanding vaccine uptake in these populations, where authorities and health services have been experienced as untrustworthy in the past (with little improvement during the pandemic). Providing more information may improve vaccine uptake somewhat; however, improved trustworthiness of health services for GRT communities is essential to increase vaccine coverage.

FUNDING

This paper reports on independent research commissioned and funded by the National Institute for Health Research (NIHR) Policy Research Programme. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health and Social Care or its arm's length bodies, and other Government Departments.

Machine learning in bail decisions and judges' trustworthiness

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three different aspects of judges' trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness.

Basic Geo-Spatial Data Literacy Education for Economic Applications

Geospatial data literacy is of paramount importance in an increasingly digital business world. Especially in economic decision-making processes, the ability to judge the trustworthiness of pertinent data sets is inevitable for reliable decisions. Thus, geospatial competencies need to supplement the university's teaching syllabus of economic degree programmes. Even if these programmes already have a lot of content, it is worth adding geospatial topics to educate students as skilled young experts, being geospatially literate. This contribution shows an approach on how to sensitise students and teachers with an economics background to understand the origin of geospatial data sets, their specific nature, their quality and how to gain geospatial data sets with a particular focus on sustainable economics applications. It proposes a teaching approach for educating students on geospatial characteristics of data, making them aware of spatial reasoning and spatial thinking. Especially it is vital to give them an impression of the manipulating nature of maps and geospatial visualisations. The aim is to show them the power of geospatial data and map products for research in their specific thematic field. The presented teaching concept originates from an interdisciplinary data literacy course geared to students other than geospatial sciences. It incorporates elements of a flipped classroom and a self-learning tutorial. This paper shows and discusses the results of the implementation of the course. Positive exam results imply that the teaching concept provides a suitable way to impart geospatial competencies to students belonging other than geo-related subjects.

Ethics 4.0: Ethical Dilemmas in Healthcare Mediated by Social Robots

This study examined people's moral judgments and trait perception toward a healthcare agent's response to a patient who refuses to take medication. A sample of 524 participants was randomly assigned to one of eight vignettes in which the type of healthcare agent (human vs. robot), the use of a health message framing (emphasizing health-losses for not taking vs. health-gains in taking the medication), and the ethical decision (respect the autonomy vs. beneficence/nonmaleficence) were manipulated to investigate their effects on moral judgments (acceptance and responsibility) and traits perception (warmth, competence, trustworthiness). The results indicated that moral acceptance was higher when the agents respected the patient's autonomy than when the agents prioritized beneficence/nonmaleficence. Moral responsibility and perceived warmth were higher for the human agent than for the robot, and the agent who respected the patient's autonomy was perceived as warmer, but less competent and trustworthy than the agent who decided for the patient's beneficence/nonmaleficence. Agents who prioritized beneficence/nonmaleficence and framed the health gains were also perceived as more trustworthy. Our findings contribute to the understanding of moral judgments in the healthcare domain mediated by both healthcare humans and artificial agents.

Children show neural sensitivity to facial trustworthiness as measured by fast periodic visual stimulation

Adults exhibit neural responses over the visual occipito-temporal area in response to faces that vary in how trustworthy they appear. However, it is not yet known when a mature pattern of neural sensitivity can be seen in children. Using a fast periodic visual stimulation (FPVS) paradigm, face images were presented to 8-to-9-year-old children (an age group which shows development of trust impressions; N = 31) and adult (N = 33) participants at a rate of 6 Hz (6 face images per second). Within this sequence, an 'oddball' face differing in the level of facial trustworthiness compared to the other faces, was presented at a rate of 1 Hz (once per second). Children were sensitive to variations in facial trustworthiness, showing reliable and significant neural responses at 1 Hz in the absence of instructions to respond to facial trustworthiness. Additionally, the magnitude of children's and adults' neural responses was similar, with strong Bayesian evidence that implicit neural responses to facial trustworthiness did not differ across the groups, and therefore, that visual sensitivity to differences in facial trustworthiness can show mature patterns by this age. Thus, nine or less years of social experience, perceptual and/or cognitive development may be sufficient for adult-like neural sensitivity to facial trustworthiness to emerge. We also validate the use of the FPVS methodology to examine children's implicit face-based trust processing for the first time, which is especially valuable in developmental research because this paradigm requires no explicit instructions or responses from participants.

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in image synthesis, Generative Adversarial Networks (GANs), and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.

TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation

Recently, deep networks have shown impressive performance for the segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving slow to transition to widespread use in medical clinics because of robustness issues leading to low trust of clinicians to their results. Predicting run-time quality of segmentation masks can be useful to warn clinicians against poor results. Despite its importance, there are few studies on this problem. To address this gap, we propose a quality control method based on the agreement across decoders of a multi-view network, TMS-Net, measured by the cosine similarity. The network takes three view inputs resliced from the same 3D image along different axes. Different from previous multi-view networks, TMS-Net has a single encoder and three decoders, leading to better noise robustness, segmentation performance and run-time quality estimation in our experiments on the segmentation of the left atrium on STACOM 2013 and STACOM 2018 challenge datasets. We also present a way to generate poor segmentation masks by using noisy images generated with engineered noise and Rician noise to simulate undertraining, high anisotropy and poor imaging settings problems. Our run-time quality estimation method show a good classification of poor and good quality segmentation masks with an AUC reaching to 0.97 on STACOM 2018. We believe that TMS-Net and our run-time quality estimation method has a high potential to increase the thrust of clinicians to automatic image analysis tools.

Trustworthy and Reliable Deep Learning-based Cyberattack Detection in Industrial IoT

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics such as trust, privacy, security, reliability, resilience and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this paper, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep learning-based Pyramidal Recurrent Units (PRU) and Decision Tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The non-linear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on fifteen datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

The independent effects of source expertise and trustworthiness on retraction believability: The moderating role of vested interest

Past research suggests that the trustworthiness of a source issuing a retraction of misinformation impacts retraction effectiveness, whereas source expertise does not. However, this prior research largely used expert sources who had a vested interest in issuing the retraction, which might have reduced the impact of those expert sources. We predicted that source expertise can impact a retraction's believability independent of trustworthiness, but that this is most likely when the source does not have a vested interest in issuing a retraction. Study 1 demonstrated that retractions from an expert source are believed more and lead to less continued belief in misinformation than retractions from an inexpert source while controlling for perceptions of trustworthiness. Additionally, Study 1 demonstrated that this only occurs when the source had no vested interest in issuing the retraction. Study 2 found similar effects using a design containing manipulations of both expertise and trustworthiness. These results suggest that source expertise can impact retraction effectiveness and that vested interest is a variable that is critical to consider when determining when this will occur.

Exploring member trust in German community-supported agriculture: a multiple regression analysis

Opaque value chains as well as environmental, ethical and health issues and food scandals are decreasing consumer trust in conventional agriculture and the dominant food system. As a result, critical consumers are increasingly turning to community-supported agriculture (CSA) to reconnect with producers and food. CSA is often perceived as a more sustainable, localized mode of food production, providing transparent production or social interaction between consumers and producers. This enables consumers to observe where their food is coming from, which means CSA is considered suitable for building trust in food (production). However, it remains unclear how exactly members' trust in 'their' farmers is built. To determine the factors that predict members' trust in CSA and its farmers, and the importance of these factors when compared to each other, we conducted a quantitative study among CSA members in Germany and applied a multiple regression model (n = 790). The analysis revealed that trust in CSA and its farmers is influenced by "reputation", "supply of information", "direct social interaction" and the "duration of CSA membership". Other factors such as the "certification status of the CSA farm" and "attitudes toward organic certification" did not significantly predict trust. We conclude that producers' willingness to be transparent already signals trustworthiness to CSA members and is more important to members than formal signals. Other actors within the food system could learn from CSA principles and foster a transition toward a more regionalized value-based food system to help restore agriculture's integrity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10460-022-10386-3.

Trustworthiness judgments and Borderline Personality Disorder: an experimental study on the interplay of happiness and trustworthiness appraisals and the effects of wearing face masks during the Covid-19 pandemic in Germany

BACKGROUND

Judging positive emotional states or the trustworthiness of others is important for forming and maintaining social affiliations. Past studies have described alterations in these appraisal processes in Borderline Personality Disorder (BPD), which might have been exacerbated during the Covid-19 pandemic by the requirement to wear face masks. In the present study, we investigated in an online-survey a) whether social judgments are particularly strongly affected in individuals with BPD when they have to judge happiness and trustworthiness in facial stimuli covered by a mask, b) whether appraising a positive emotional state affects trustworthiness appraisals differentially in BPD and healthy individuals and c) whether social judgments are related to how individuals with BPD experience wearing masks during the pandemic.

METHODS

Participants (67 HC, 75 BPD) judged happiness and trustworthiness of faces with calm expression with and without masks. Additionally, data on participants' confidence in their judgments, the experience of the burden induced by wearing masks, the protective benefits of masks, and compliance to wearing masks were collected.

RESULTS

Happiness and trustworthiness were evaluated less confidently and less intense in the BPD group compared to HC. Masks reduced happiness and trustworthiness ratings in both groups. Lower happiness appraisals contributed to lower trustworthiness appraisals except for those with BPD and low levels of symptom severity. Lower trustworthiness ratings were associated with a higher burden, attributing a lower benefit to masks and lower compliance with wearing masks in BPD.

CONCLUSIONS

Masks do not exacerbate deficits in social judgments. However, lower trustworthiness appraisals in general were linked with more negative evaluations of wearing masks in the BPD group.

TRIAL REGISTRATION

The aims and hypotheses were preregistered together with the design and planned analyses ( https://aspredicted.org/f5du7.pdf ). For findings of an additionally preregistered research question on the impact of adverse childhood experiences see supplementary material.

Explainable, trustworthy, and ethical machine learning for healthcare: A survey

With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. Along with highlighting security, safety, and robustness challenges that hinder the trustworthiness of ML, we also discussed the ethical issues arising because of the use of ML/DL for healthcare. We also describe how explainable and trustworthy ML can resolve all these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

[The role and significance of trust for successful institutional risk communication]

The success of institutional crisis and risk communication is based on an open and dialogue-oriented communication policy as well as on a congruence between the expectations of all parties involved and their fulfillment. Central to communication with other actors and with the population is a mutual relationship of trust. This paper describes factors that are instrumental in determining whether and to what extent institutions can establish a basis of trust and credibility. It discusses how trustworthy risk communication can succeed even in times of crisis.Successful risk communication is tied to a process that conveys credibility and competence through openness of results, convincing communication of scientific evidence, the inclusion of plural values and criteria, and the synthesis of system and orientation knowledge. Given the uncertainty of knowledge and the confusion of communication processes in society, this task is not easy to accomplish. Despite these difficulties, institutions of risk assessment and regulation can build trust and perpetuate it over time through skillful coalition-building with organizations and groups with high trust potential, transparent, open-ended forms of communication, and the involvement of stakeholders and affected individuals in risk management.

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

Ecologies of Public Trust: The NHS COVID-19 Contact Tracing App

In April 2020, close to the start of the first U.K. COVID-19 lockdown, the U.K. government announced the development of a COVID-19 contact tracing app, which was later trialled on the U.K. island, the Isle of Wight, in May/June 2020. United Kingdom surveys found general support for the development of such an app, which seemed strongly influenced by public trust. Institutions developing the app were called upon to fulfil the commitment to public trust by acting with trustworthiness. Such calls presuppose that public trust associated with the app can emerge if the conditions for trustworthiness are met and that public trust is simplistic, i.e., linearly the sum of each member of the publics' individual - U.K. government trust relationship. Drawing on a synthesis of the trust literature and fifteen interviews with members of the public trialling the app on the Isle of Wight, this paper aims to explore what trust mechanisms and relationships are at play when thinking about public trust in the context of the U.K. COVID-19 app. We argue that public trust is a complex social phenomenon and not linearly correlated with institutional trustworthiness. As such, attention needs to widen from calls for trustworthy infrastructures as a way to build public trust, to a deeper understanding of those doing the trusting; in particular, what or whom do people place their trust in (or not) when considering whether using the app and why. An understanding of this will help when trying to secure public trust during the implementation of necessary public health measures.

Unmasking the Ethics of Public Health Messaging in a Pandemic

Uncertainty is inherent in new and unexpected viral outbreaks such as the current COVID-19 pandemic. It imposes challenges for health officials in soliciting cooperative behavioural changes based on incomplete information. In this paper, we use evolving mask recommendations in the United States as an example to analyse the ethical importance and practical demonstration of trustworthiness in pandemic messaging and decision-making. We argue that responsible public health interventions in the time of uncertainties requires explicit intersecting ethical considerations both in action and in communication to promote trustworthiness. First, as public health decisions have to be made in the face of incomplete and evolving data, health officials need to exhibit competence while committing to epistemic humility. They can explain the methods used in making and updating mask recommendations as well as explicitly acknowledge the need to incorporate sociocultural and other contextual considerations in translating scientific data into mask recommendations. Second, officials and agencies must uphold and communicate decisional transparency as part of their effort to demonstrate accountability and promote the public's understanding of the evolving pandemic. Third, especially since both the pandemic and mask recommendations may have disparate impact on different populations, officials should start with the fair implementation of the least restrictive measures that can help reduce harm.

Trustworthy Artificial Intelligence in Medical Imaging

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.

Users evaluation of a Spanish eHealth pediatric website

BACKGROUND

Parents use the Internet to take decisions about their children's health, but few resources have focused on eHealth technology evaluations from their point of view.

OBJECTIVE

The main aim of this research was to evaluate a Spanish eHealth pediatric website for parents.

METHODS

A previously validated web questionnaire was used to evaluate five domains: usability, utility, trust and confidence, well-child section and accessibility of the website. Univariate, bivariate and multiple linear regression analyses were performed.

RESULTS

516 users participated in the research and rated the website as usable, useful, trustworthy and accessible. Higher scores were given by the participants who relied most on the Internet for taking decisions about health; by the participants who used a smartphone to access the pediatric website; by the participants who knew the website the longest; and by the participants who had accessed it more times. No differences in the evaluations of the website were found regarding age, education level or household income of the participants.

CONCLUSIONS

eHealth pediatric websites, written by a pediatrician in an easy to understand language, can be perceived as usable, trustworthy, useful and accessible by their users and consequently help them with their decisions making. Some characteristics of the users are associated with a better perception of these websites.

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

Improving the trustworthiness of findings from nutrition evidence syntheses: assessing risk of bias and rating the certainty of evidence

Suboptimal diet is recognized as a leading modifiable risk factor for non-communicable diseases. Non-randomized studies (NRSs) with patient relevant outcomes provide many insights into diet-disease relationships. Dietary guidelines are based predominantly on findings from systematic reviews of NRSs-mostly prospective observational studies, despite that these have been repeatedly criticized for yielding potentially less trustworthy results than randomized controlled trials (RCTs). It is assumed that these are a result of bias due to prevalent-user designs, inappropriate comparators, residual confounding, and measurement error. In this article, we aim to highlight the importance of applying risk of bias (RoB) assessments in nutritional studies to improve the credibility of evidence of systematic reviews. First, we discuss the importance and challenges of dietary RCTs and NRSs, and provide reasons for potentially less trustworthy results of dietary studies. We describe currently used tools for RoB assessment (Cochrane RoB, and ROBINS-I), describe the importance of rigorous RoB assessment in dietary studies and provide examples that further the understanding of the key issues to overcome in nutrition research. We then illustrate, by comparing the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach with current approaches used by United States Department of Agriculture Dietary Guidelines for Americans, and the World Cancer Research Fund, how to establish trust in dietary recommendations. Our overview shows that the GRADE approach provides more transparency about the single domains for grading the certainty of the evidence and the strength of recommendations. Despite not increasing the certainty of evidence itself, we expect that the rigorous application of the Cochrane RoB and the ROBINS-I tools within systematic reviews of both RCTs and NRSs and their integration within the GRADE approach will strengthen the credibility of dietary recommendations.

Does the ownership of health website matter? A cross-sectional study on Chinese consumer behavior

BACKGROUND

Ownership has significant impact on website motivation. Consumers may heavily rely on the health website ownership cue when assessing credibility and making behavioral response toward health information on it. Health websites were primarily divided into four different ownership types (i.e., governmental, organizational, commercial, and personal) in China's context. However, research on Chinese consumer behavior toward different ownership types of health websites is scarce.

OBJECTIVES

To investigate the most credible and most commonly used health website ownership type among Chinese consumers, and to identify the influencing factors on perceived credibility, and actual usage of health websites.

METHODS

A cross-sectional survey of 1653 participants was conducted in 3-tier hospitals in 3 cities with different income levels. Multinomial logistic regression analyses were used to identify factors influencing Chinese consumers' perceived credibility and actual use of health websites.

RESULTS

The most credible health website was the organizational, followed by the governmental, commercial, and personal. The most commonly used health website was the commercial, followed by the organizational, governmental, and personal. Individuals in medium-income and low-income cities were more likely than those in high-income cities to trust and use non-governmental health websites. Compared to the governmental health website, consumers of high-level hospitals were less likely than those of primary hospitals to trust and use personal health websites. Compared to the governmental health website, high-income individuals were more likely than low-income individuals to trust the personal health website, and use the organizational and commercial health website.

CONCLUSIONS

Both Chinese consumers' perceived credibility and actual use of health website varied by ownership, and there was a gap between perceived credibility and actual usage of health website. Most sociodemographic factors had no statistically significant correlations with perceived credibility and actual usage of health website. City income level, consumer type and consumer income level were significantly associated with perceived credibility, actual usage of health websites.

Reciprocal Trust as an Ethical Response to the COVID-19 Pandemic

The COVID-19 pandemic has generated a range of responses from countries across the globe in managing and containing infections. Considerable research has highlighted the importance of trust in ethically and effectively managing infectious diseases in the population; however, considerations of reciprocal trust remain limited in debates on pandemic response. This paper aims to broaden the perspective of good ethical practices in managing an infectious disease outbreak by including the role of reciprocal trust. A synthesis of the approaches drawn from South Korea and Taiwan reveals reciprocal trust as an important ethical response to the COVID-19 pandemic. Reciprocal trust offers the opportunity to reconcile the difficulties arising from restrictive measures for protecting population health and individual rights.

Motor-response execution versus inhibition alters social-emotional evaluations of specific individuals

Social-emotional evaluations of unfamiliar people are negatively impacted by ignoring or withholding motor-responses from images that depict them; an effect attributed to the propensity of inhibition to affectively devalue associated stimuli. Prior findings suggest that the social-emotional consequences of inhibition may operate on category-level representations that impact all members of a corresponding group. Here we assess whether such social-emotional consequences of motor-response action versus inaction also operate on item-level representations of specific individuals. Participants memorized individual identities of a group of fellow students before completing a Go/No-go response-inhibition task designed to associate item-level representations of each previously-memorized person with action (Go trials) or inaction (No-go trials). Social identities associated with action were consistently rated as more trustworthy in subsequent evaluations than those associated with inaction. This suggests that the social-emotional consequences of motor-response execution versus inhibition can operate on item-level stimulus representations in memory.

Trustworthiness and electrocortical processing of emotionally ambiguous faces in student police officers

Perceptions of emotional facial expressions and trustworthiness of others guides behavior and has considerable implications for individuals who work in fields that require rapid decision making, such as law enforcement. This is particularly complicated for more ambiguous expressions, such as 'neutral' faces. We examined behavioral and electrocortical responses to facial expressions in 22 student police officers (18 males; 23.2 ± 3.63 years). Participants completed an emotional face appraisal task that involved viewing three expressions (fearful, neutral, happy) and were asked to identify the emotion and rate the trustworthiness of each face. The late positive potential (LPP), an event-related potential that tracks emotional intensity and/or salience of a stimulus, was measured during the task. Overall, participants rated neutral faces similarly to fearful faces and responded fastest to these expressions. Neutral faces also elicited a robust late LPP response that did not differ from LPP to fearful or happy faces, and there was substantial individual variation in trustworthiness ratings for neutral faces. Together, 'neutral' facial expressions elicited similar trustworthiness ratings to negatively-valenced stimuli. Brain and behavioral responses to neutral faces also varied across student officers; thus, encounters with ambiguous faces in the field may promote increased perceived threat in some officers, which may have real-world consequences (e.g., decision to shoot, risk of psychopathology).

Can you believe it? An investigation into the impact of retraction source credibility on the continued influence effect

The continued influence effect refers to the finding that people often continue to rely on misinformation in their reasoning even if the information has been retracted. The present study aimed to investigate the extent to which the effectiveness of a retraction is determined by its credibility. In particular, we aimed to scrutinize previous findings suggesting that perceived trustworthiness but not perceived expertise of the retraction source determines a retraction's effectiveness, and that continued influence arises only if a retraction is not believed. In two experiments, we found that source trustworthiness but not source expertise indeed influences retraction effectiveness, with retractions from low-trustworthiness sources entirely ineffective. We also found that retraction belief is indeed a predictor of continued reliance on misinformation, but that substantial continued influence effects can still occur with retractions designed to be and rated as highly credible.

Modernizing innovation governance to meet policy ambitions through trusted environments

A vision for modernization of nanotechnology innovation governance is a Safe Innovation Approach (SIA). SIA combines two concepts: Safe-by-Design (SbD) and Regulatory Preparedness (RP). SbD aims to motivate industry to integrate safety considerations early in the innovation process and onwards. RP aspires to improve the anticipation capabilities of regulators and develop legislation that can keep pace with innovations. The pace, scope and complexity of nanotechnology present novel challenges for governance, especially law and regulation. A possible option forward for nanotechnology is to move towards a more goal-based governance system including anticipatory regulation. Anticipatory regulation and experimentation can be considered as an agile approach with emphasis on flexibility, collaboration and innovation. SIA can be seen as part of experimentation in support of agile regulatory practices. A trusted environment is needed in which innovators, regulators and other stakeholders are motivated to understand each other's concerns and together develop solutions to anticipate and address safety whilst also facilitating the development of safe, sustainable and socially beneficial innovations. Trust drivers to facilitate trusted environments include focusing on the public interest, competence, respect, integrity, inclusion, fairness and openness. Here, we explore the concept of building trusted environments in the context of the SIA for nanotechnologies.

Difficult name, cold man: Chinese names, gender stereotypicality and trustworthiness

Names can play an important role in forming first impressions. While much of the literature has demonstrated how alphabet-based names influence impression formation, little is known about how character-based names (e.g., Chinese names) affect interpersonal trust. Across six studies, we demonstrated that a difficult-to-recognise Chinese name with less frequently used characters activated masculine perception, which in turn decreased trust in the name holder. The masculine inferences from difficult names were replicated across within-subjects (Study 1a and 1b) and between-subjects judgements and maintained irrespective of normative knowledge about difficult names as male names (Study 1c). The mediation of gender stereotypicality was manifested in both measured spontaneous gender inference (Study 2a and Study 2b) and manipulated gender information (Study 2c). The effects of recognisability on masculine and trust perceptions were independent of pronunciationability (Study 2b). This research extends previous research by revealing the implications of character-based names and pictographic language on the feeling-as-information theory, also in terms of interpersonal contexts.

Lay concepts of source likeability, trustworthiness, expertise, and power: A prototype analysis

Previous research on persuasion has used researcher-generated exemplars to manipulate source characteristics such as likeability, trustworthiness, expertise, or power. This approach has been fruitful, but it relies to some degree on an overlap between researcher understanding of these variables and lay understanding of these variables. Additionally, these exemplar manipulations may have unintentionally affected multiple characteristics and may be limited to certain topics or time periods. In the current work, we sought to provide persuasion researchers with a methodological tool to increase construct and potentially external validity by conducting a prototype analysis of the four traditional source characteristics: likeability, trustworthiness, expertise, and power. This bottom-up approach provided insight into the ways in which recipients perceive sources and allowed us to examine relations between the characteristics. Moving forward, a bottom-up understanding of source characteristics will allow researchers to more effectively develop manipulations that might transcend time and topic as well as isolate their effects to the intended source characteristic.

Screening for scoliosis - New recommendations, old dilemmas, no straight solutions

This opinion review considers the prevailing question of whether to screen or not to screen for adolescent idiopathic scoliosis. New and improved standards of people-oriented care and person-centredness, as well as improved principles of preventive screening and guideline development, have been postulated and implemented in health care systems and cultures. Recommendations addressing screening for scoliosis differ substantially, in terms of their content, standards of development and screening principles. Some countries have discontinued issuing recommendations. In the last decade, a number of updated and new recommendations and statements have been released. Systematically developed guidelines and recommendations are confronted by consensus and opinion-based statements. The dilemmas and discrepancies prevail. The arguments concentrate on the issues of the need for early detection through screening in terms of the effectiveness of early treatment, on costs and cost-effectiveness issues, scientific and epidemiologic value of screenings, and the credibility of the sources of evidence. The problem matter is of global scale and applies to millions of people. It regards clinical and methodological dilemmas, but also the matter of vulnerable and fragile time of adolescence and, more generally, children's rights. The decisions need to integrate people's values and preferences - screening tests need to be acceptable to the population, and treatments need to be acceptable for patients. Therefore we present one more crucial, but underrepresented in the discussion, issue of understanding and implementation of the contemporary principles of person-centred care, standards of preventive screening, and guideline development, in the context of screening for scoliosis.

Knowledge and information credibility evaluation strategies regarding COVID-19: A cross-sectional study

BACKGROUND

The novel coronavirus disease (COVID-19) pandemic has not only caused significant challenges for health systems worldwide, but also fueled a surge in misinformation. Nurses as frontline health care providers should be equipped with the most accurate information on COVID-19.

PURPOSE

This study examines nurses' knowledge and strategies of information credibility sourcing.

METHOD

A cross-sectional survey among nurses and laypersons with no health care background. The questionnaire dealt with knowledge and ability assess credibility of COVID-19 information.

FINDINGS

Nurses' knowledge of COVID-19 preventative behaviors was significantly higher than that of laypersons; however, there was no difference in science-based knowledge of COVID-19. In contrast to laypersons, nurses in this study were better able to discern the credibility of health-related information about COVID-19 than laypersons. Yet they rarely used scientific criteria in evaluating conflicting information.

DISCUSSION

Given the importance of assessing the credibility of information, both information literacy skills and science-based knowledge about COVID-19 should be offered.

A stronger relationship between reward responsivity and trustworthiness evaluations emerges in healthy aging

Older adults (OA) evaluate faces to be more trustworthy than do younger adults (YA), yet the processes supporting these more positive evaluations are unclear. This study identified neural mechanisms spontaneously engaged during face perception that differentially relate to OA' and YA' later trustworthiness evaluations. We examined two mechanisms: salience (reflected by amygdala activation) and reward (reflected by caudate activation) - both of which are implicated in evaluating trustworthiness. We emphasized the salience and reward value of specific faces by having OA and YA evaluate ingroup male White and outgroup Black and Asian faces. Participants perceived faces during fMRI and made trustworthiness evaluations after the scan. OA rated White and Black faces as more trustworthy than YA. OA had a stronger positive relationship between caudate activity and trustworthiness than YA when perceiving ingroup, but not outgroup, faces. Ingroup cues might intensify how trustworthiness is rewarding to OA, potentially reinforcing their overall positivity.

Data modelling consumer-generated content usage for apparel shopping

This data article presents raw inferential statistical data which determine the use of consumer-generated content for online fashion apparel shopping among young adult consumers in South Africa. The data was gathered from consumers within the Gauteng Province metropolitan area of Johannesburg. Structural equation modelling approach using partial least squares statistical software (Smart PLS) was used to test the posited hypotheses in the conceptual research model. Structured questionnaires were distributed to consumers within the Johannesburg Metropolitan area. This data set show that perceived usefulness, perceived trustworthiness, knowledge and Competence has a major statistical impact on attitude towards the use of consumer generated content. The data also suggested a statistically significant relationship between attitude and intention to use consumer generated content as a source of information for online apparel shopping. In addition, the data showed that the perceived usefulness and trustworthiness had a statically marginal impact on usage intention. The data also shows that the perceived enjoyment of user generated content by users has had a negative and statically insignificant impact on attitude, while attitude has had a positive and significant effect on consumer behavioural intentions.

Effects of a dopamine agonist on trusting behaviors in females

Trust is central to bonding and cooperation. In many social interactions, individuals need to trust another person exclusively on the basis of their subjective impressions of the other's trustworthiness. Such impressions can be formed from social information from faces (e.g., facial trustworthiness and attractiveness) and guide trusting behaviors via activations of dopaminergic brain regions. However, the specific dopaminergic effects on impression-based trust are to date elusive. Here, in a double-blind, placebo-controlled, within-subject design, we administrated a D2/D3 dopamine agonist (pramipexole) to 28 healthy females who subsequently played a one-shot trust game with partners of varying facial trustworthiness. Our results show that by minimizing facial attractiveness information, we could isolate the specific effects of facial trustworthiness on trust in unknown partners. Despite no modulation of trustworthiness impressions, pramipexole intake significantly impacted trusting behaviors. Notably, these effects of pramipexole on trusting behaviors interacted with participants' hormonal contraceptive use. In particular, after pramipexole intake, trust significantly decreased in hormonal contraceptive non-users. This study fills an important gap in the experimental literature on trust and its neural dynamics, unearthing the cognitive and neural modulations of trusting behaviors based on trustworthiness impressions of others.

Investigating the effect of trustworthiness on instruction-based reflexivity

Unlike other species, humans are capable of rapidly learning new behavior from a single instruction. While previous research focused on the cognitive processes underlying the rapid, automatic implementation of instructions, the fundamentally social nature of instruction following has remained largely unexplored. Here, we investigated whether instructor trustworthiness modulates instruction implementation using both explicit and reflexive measures. In a first preregistered study, we validated a new paradigm to manipulate the perceived trustworthiness of two different virtual characters and showed that such a manipulation reliably induced implicit associations between the virtual characters and trustworthiness attributes. Moreover, we show that trustworthy instructors are followed more frequently and faster. In two additional preregistered experiments, we tested if trustworthiness towards the instructor influenced the cognitive processes underlying instruction implementation. While we show that verbally conveyed instructions led to automatic instruction implementation, this effect was not modulated by the trustworthiness of the instructor. Thus, we succeeded to design and validate a novel trustworthiness manipulation (Experiment 1) and to create a social variant of the instruction-based reflexivity paradigm (Experiments 2 and 3). However, this instruction-based reflexivity effect was not modulated by the instructors' trustworthiness.