Truthful Information Research Articles

Recomendaciones para la investigación psicológica con personas transgénero: Una aproximación desde una experiencia en Chile

Prejudice and discrimination have important effects on the well-being and quality of life of trans people. Hence, investigating with said population implies, in addition to actions to obtain relevant and truthful information, having the necessary actions to attend to their needs and peculiarities. These are populations that are difficult to access (hard-to-reach), stigmatized, in which there is no defined sampling frame, that have physical and mental health indicators lower than those of any other group with which we have worked and that, therefore, it is necessary to take extreme the precautions not to reproduce types of relationships that may be harmful or offensive. The purpose of the present article was to systematize part of our field experience with transgender people, so that it can be useful for those who study with this population. These recommendations aim to provide some conceptual, methodological and ethical guidelines, useful throughout the research process. In particular, we made a series of recommendations that cover from the moment of taking conceptual decisions about the design of the study, through contact with the organizations, until the completion of the field work and subsequent stage of dissemination of results. In particular, a series of recommendations are made regarding: specifying the definition of the group with which one works, recording the variety of gender identifications as people present them, using to sampling methods for hard-to-reach populations or hidden populations, deliver a retribution for the participation (consider participants as experts to be compensated for their work), including activists as experts in every stage of the study (study design, field work, data analysis and dissemination of results), piloting the instruments in specific population, preferably opt for group applications modality and continue with a debriefing, have specific assistance for participants during the application (special needs derived from their age, educational level, reduced mobility, among others), use non-stigmatizing and inclusive language, call together with trans organizations (in order to establish greater trust in the population), attend to a mobility of the population (so as not to replicate the sample in different places), carry out a committed listening and contemplate referral protocols (if necessary given the type of situations described by the participants and their current degree of elaboration), consider different trajectories of transition, conduct inter-analysis sessions and disseminate appropriately within the transgender population. These propositions are discussed in terms of their effects on trans communities, their usefulness and ethical scope, their articulation with the general requirements of all research, and their implications for activism. On the other hand, these recommendations can serve as criteria for evaluating the ethical dimension of research projects with trans people. It may therefore be important to project reviewers, editors, and reviewers of manuscripts submitted for publication. The perspective that we assume in this work is aligned with those called "evidence-based activism", which implies articulating a variety of knowledge, scientifically accredited and of an "experiential" type, in order to explore situations in which the team members Research staff are part of networks that involve them personally in their study topics. It is precisely the relationship with groups that work for the rights of minorities, activists and members of these groups is essential, since they have knowledge acquired through their experience as part of discriminated groups and they can contribute to the mobilization of knowledge in the governance of health issues.

InPPTD: A Lightweight Incentive-Based Privacy-Preserving Truth Discovery for Crowdsensing Systems

Recently, truth discovery in crowdsensing systems has received considerable attention with its appealing features for extracting truthful information from multiple unreliable data sources. However, it also poses new challenges to the issues of privacy and security. On the one hand, workers’ sensed data can be used to infer their privacy. On the other hand, workers may be selfish and lazy, especially in the Internet-of-Things environment, devices are usually resource constrained, so they may dishonestly execute the costly sensing task so as to reduce resource consumption, or even break the protocol to obtain illegal rewards. Although some privacy-preserving truth discovery schemes have been proposed, they still cannot achieve strong privacy protection while keeping efficiency on the worker side, and still has no efficient incentive mechanism to persuade workers to participate in the system operations. In this article, we propose an incentive-based privacy-preserving truth discovery framework, named InPPTD. By adopting the Paillier homomorphic cryptosystem and two noncolluding servers, InPPTD not only effectively protects workers’ sensed data information but also preserves the privacy of these workers’ weight information. Meanwhile, a weight-based incentive mechanism is introduced in InPPTD to reduce the number of lazy workers. Security and performance analysis shows that InPPTD can guarantee stronger security features, while also ensure efficiency in terms of computation and communication overhead.

The Mortal Coil of Covid-19, Fake News, and Negative Epistemic Postdigital Inculcation

The Covid-19 pandemic has engendered turmoil around our globe, rendering an urgent need for accurate, truthful information as a life-saving resource for humanity. However, coinciding with this global, deadly pandemic is the proliferation of fake news. While pandemics and fake news are not new phenomena, an unprecedented time in history is presently unfolding when considered with the postdigital era. Digital media enables the prolific repetitious spread of fake news during crises when accurate and truthful information is necessary. Consequently, the ability of humans to discern between fact and fiction diminishes. It has resulted in some people making life-ending decisions based on their exposure to fake news. In this article, I define a primarily ignored and invisible epistemological process at work: negative epistemic postdigital inculcation, that, while has been at work with the rise of modern digital media, has primarily become visible because of the interrelationships between implicit learning, Covid-19, fake news, and digital media. While the inculcation outlined in this paper occurs mostly outside of our awareness, I discuss a role for education in helping reduce the ensuing mortal coil of fake news.

Designing a Revenue Sharing Contract under Information Asymmetry

In a supply chain coordinated by a revenue sharing contract, under-reporting of sales revenue has been a common practice amongst retailers who always have private information about the market demand. In this article, we aim to design a mechanism to mitigate this problem. One may design a contract to elicit truthful information from the retailer while maximizing supplier’s payoff. However, we find that such contracts fail to coordinate the supply chain, when the market demand is high. Hence, we study an audit-based revenue sharing contract. First, we design a laboratory experiment to investigate the impact of retailer’s decisions on the subsequent choices made by the supplier. We find that the audit probability chosen by the supplier increases with the gap between retailer’s order quantity and the sales reported by the retailer. We follow this up with a simulation experiment which incorporates the findings of our laboratory experiment. Audit cost and the penalty announced by the supplier for not reporting true sales turned out to be important in making decisions for both the players. We also find the threshold auditing cost beyond which auditing is not economically viable for the supplier.

Management Accounting.

Healthcare institutions are business systems that provide different medical products and services to improve the health and quality of life of users of products and services. First of all, this is reflected in the differing complexities of the business, but also of the products and services they offer, as well as the different costs they cause. Thus, some products or services cause high indirect costs, while others require higher direct costs. For health care managers to make the right business decisions, they must continually have timely, quality and truthful information at their disposal. This information is provided by the management accounting of the institutions. This paper aims to present management accounting activities that enable the acquisition of information necessary for decision making.

Information on COVID-19 and Psychological Distress in a Sample of Non-Health Workers during the Pandemic Period.

Methods by which the population should be informed when going through a pandemic such as COVID-19 have been questioned because of its influence on the adoption of preventive measures and its effects on mental health. Non-health workers are at risk of psychological distress from exposure to contaminated people or materials or by having to stay at home and adapt their activity to telework. The objective of the study is to analyze information the public receives about COVID-19 and its influence on their level of distress. For this, 1089 questionnaires from non-health workers were collected online between 26 March and 26 April 2020 in Spain, and analysed and distributed by snowball effect. 492 participants carried out essential activities away from home, and 597 did so from home. They were surveyed about information received about COVID-19 regarding its source, time, assessment, or the beliefs expressed in it. Mental health was also measured with Goldberg’s General Health Questionnaire (GHQ-12). The classification and regression tree (CART) method was used to design a binary tree with sample cases. It has been found that the time spent learning about COVID-19 and the level of knowledge about symptoms, pathways, prevention, treatment, or prognosis are associated with the level of distress, where 25% of participants were found to have spent more than 3 h daily on this activity. Social media and television are the most widely used sources, but they are considered to be of lower quality and usefulness than official sources. There is greater confidence in healthcare professionals than in the health system, and the main concern of those working away from home is spreading the virus to family members. It has been concluded that there is a need to enhance quality and truthful information on the Internet for non-health workers due to its accessibility, which should be constantly updated, a fact which international and national public bodies, research centers, and journal publishers have begun to understand during the current pandemic. Such quality information is needed to combat distress.

Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory

In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAVs, using the framework of contract theory, an offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utility of the overloaded BS is maximized. Simulation results show that the proposed WEM approach yields a prediction error of around 10%. Compared with the expectation maximization and k-mean approaches, the WEM method shows a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with two event-driven deployment schemes based on the closest-distance and maximal-energy metrics, the proposed predictive approach enables UAV operators to provide efficient communication service for hotspot users in terms of the downlink capacity, energy consumption and service delay. Simulation results also show that the proposed method significantly improves the revenues of both the BS and UAV networks, compared with two baseline schemes.

The ethical conflict of truth, hope, and the experience of suffering: A discussion of non-disclosure of terminal illness and clinical placebos

In medical practice, physicians are often faced with tough ethical and moral dilemmas, one such example is the reoccurring conflict between a patient’s hope and the truth. This paper explores two ethical dilemmas centered on compassion and the reduction of suffering: truth-telling with terminal patients and the clinical use of placebos. In each case the disclosure of truthful information could interfere with hope and suffering relief.

O setor da Construção Civil no Brasil e a atual crise econômica

O setor da Construção Civil possui participação significativa na economia brasileira, estando diretamente ligado ao desenvolvimento e produção nacional do país. A participação do setor representou 5,3% da economia nacional nos últimos anos, com forte influência na geração de empregos. Além de políticas habitacionais e obras de infraestrutura, os megaeventos esportivos também impulsionaram o crescimento do setor. Contudo, os problemas da gestão ineficaz nas ações governamentais para realização desses eventos desencadearam na recente crise econômica e subsequente retração do setor da construção civil. O presente trabalho tem como objetivo analisar os reflexos da atual crise econômica no mercado da construção civil brasileira, iniciada em 2014. A partir das análises estatísticas foi possível avaliar o comportamento do setor bem como a relação entre os indicadores econômicos e os da construção civil. O tema possui relevância para todos os interessados no setor da construção civil que buscam informações verídicas e confiáveis. Os resultados obtidos indicam correlação forte entre o PIB do Brasil e o PIB da Construção Civil, bem como influência na taxa de desemprego. O setor da construção civil encontra-se em lenta recuperação, seguindo em paralelo com a recuperação econômica do país, sendo o setor que mais sofreu impacto da crise e apresentou uma retração de 32,6% nos últimos seis anos.

Vaping and oral health

Electronic cigarettes also known as e-cigarettes, which deliver a vaporised smoke of nicotine and promise less toxicants than conventional cigarette smoke. However, studies suggest e-cigarettes are not completely harmless but are less dangerous than traditional cigarettes. The promise of help in smoking cessation effectiveness is still unproven and requires further evidence and studies. There are ongoing studies on deleterious oral and systemic health effects. Because of the rising popularity of vaping, it is absolutely vital that dental professionals must stay informed to provide patients truthful information as new data emerge. Keywords: Ecigarette, Vaping, Smoke, Cigarette, Oral health, Effect.

Same-Day Consent for Regional Anesthesia Clinical Research Trials: It's About Time.

Same-Day Consent for Regional Anesthesia Clinical Research Trials: It's About Time.

Verbal cues to deceit when lying through omitting information

BackgroundLying through omitting information has been neglected in verbal lie detection research. The task is challenging: Can we decipher from the truthful information a lie teller provides that s/he is hiding something? We expected this to be the case because of lie tellers’ inclination to keep their stories simple. We predicted lie tellers to provide fewer details and fewer complications than truth tellers, the latter particularly after exposure to a Model Statement.MethodA total of 44 truth tellers and 41 lie tellers were interviewed about a conversation (debriefing interview) they had taken part in earlier. Lie tellers were asked not to discuss one aspect of that debriefing interview.ResultsResults showed that truth tellers reported more complications than lie tellers after exposure to a Model Statement.ConclusionIdeas about future research in lying through omissions are discussed.

Exploring WHO Communication during the COVID 19 Pandemic through the WHO Website Based on W3C Guidelines: Accessible for All?

Health crisis situations generate greater attention and dependence on reliable and truthful information from citizens, especially from those organisations that represent authority on the subject, such as the World Health Organization (WHO). In times of global pandemics such as COVID-19, the WHO message “health for all” takes on great communicative importance, especially from the point of view of the prevention of the disease and recommendations for action. Therefore, any communication must be understandable and accessible by all types of people, regardless of their technology, language, culture or disability (physical or mental), according to the World Wide Web Consortium (W3C), taking on special relevance for public health content. This study analysed whether the WHO is accessible in its digital version for all groups of citizens according to the widely accepted standards in the field of the Internet. The conclusion reached was that not all the information is accessible in accordance with the Web Content Accessibility Guidelines 2.1, which implies that there are groups that are, to some extent, left out, especially affecting the elderly. This study can contribute to the development of proposals and suggest ways in which to improve the accessibility of health content to groups especially vulnerable in this pandemic.

Honesty biases trustworthiness impressions.

Honesty is central to trust and trustworthiness. However, how a good reputation as honest person is learned and induces trustworthiness impressions is still unexplored. Developing a novel paradigm, we show in 3 consecutive experiments that individuals prefer trusting honest others who share truthful information, especially if honest behavior is consistent over time. Trust in honest others was independent of proximal benefits, and honest individuals were repaid for their honesty with higher trust in a subsequent interaction. Crucially, signs of dishonesty decreased trust but only in those who had not previously built a good reputation as honest partners. On the contrary, those who could establish a good reputation were trusted even when they were no longer trustworthy, suggesting that participants could not successfully track changes in trustworthiness of those with an established good reputation. These findings suggest that a good reputation biases the ability to learn the momentary trustworthiness of another person and impairs the updating of one's beliefs about the other's character for behavior revision. Computational modeling analyses indicate an asymmetry in information integration when interacting with honest individuals that likely underlies such learning impairment. By showing how a good reputation influences learning processes in trust-based interactions, our results provide a mechanistic account for biases in social learning and social interactions, advancing our understanding of social behaviors in particular and human cognition in general. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Social health recommender system

The amount of information and services available on the web represents an important opportunity for people to enrich and share knowledges. The easy access and the continuous growth of data in the web are responsible of an overload of information that leads the user to navigate in a saturated and often uninteresting and non-comprehensive environment. In the medical-clinical context, a lot of information on the web is often incomplete, inaccurate or completely wrong due to an incorrect sharing and a lack of control of the sources. In this context, recommendation systems become essential to filter truthful information and to target users with respect to their needs. The status of recent covid-19 pandemic highlighted the necessity of having health reliable sources. Health recommender systems support user in medical environment to find right information. In this contribution, we report about a project of health recommender system aiming to: (i) aggregate similar users and (ii) guarantee the truthful and quality of the extracted information through a check of the sources and a validation by the medical scientific community.

An Evolutionary Game Analysis of Internet Public Opinion Events at Universities: A Case from China

Internet public opinion events at universities in China occurred frequently, creating painful repercussions for reputation and stability of colleges and universities. To better cope with the problem, this paper explores an evolutionary mechanism of the university Internet public opinion events. Firstly, we discuss the interactions and behavior of three key participants: an Internet medium, university students as a whole, and administration. Secondly, we construct a tripartite evolutionary game model consisting of an Internet medium, student group, and university administration and then analyze and obtain the differential dynamic equations and equilibrium points. Subsequently, the evolutionary stable equilibrium is further analyzed. Finally, we employ numerical studies to examine how the tripartite behavior choices affect evolutionary paths and evolutionary equilibrium strategies. Results are derived as follows: under certain conditions, there exists an asymptotically stable equilibrium point for the tripartite evolutionary game. On the one hand, appropriate penalties and rewards should be provided to foster objectives and fair behaviors of the network medium. On the other hand, university students should be educated and guided to deal rationally with negative effects of Internet public opinion events. Moreover, online real-name authentication is an important and necessary measure. Finally, the university administration should release truthful, timely, and comprehensive information of Internet public opinion events to mitigate potential negative impacts.

Living with the cerebellar mutism syndrome: long-term challenges of the diagnosis.

After posterior fossa tumour surgery, up to 39% of children experience postoperative cerebellar mutism syndrome (CMS) characterized by mutism and other motor and cognitive impairments. There is a lack of knowledge on the patient-reported challenges and long-term needs. Consequently, no specific recommendations exist for rehabilitative and supportive interventions for patients with CMS. The aims of this study were to explore the patients' experiences related to the sequelae of CMS, to identify challenges and needs regarding support and rehabilitation in the period of growing from child to adult and to add perspectives for future developments of supportive care and rehabilitative guidelines. Ten semi-structured interviews were conducted with young adults diagnosed with CMS as children. A thematic analysis identified four themes describing challenges impacting aspects of the participants' lives. Four main themes were identified and highlight the rehabilitative need for focus on verbal and non-verbal communication skills in addition to the physical impairments. We found that brain tumour survivors with CMS can benefit from social and educational rehabilitation, straightforward and truthful information, support in structuring their everyday lives and increased public knowledge of CMS. Children with CMS face a variety of challenges affecting many aspects of their everyday lives. They should be entitled to the elements of a current rehabilitation initiative for childhood cancer to support patients' social disability and educational decline. Finally, we identified a need for an official information publication.

Public Behavior Change, Perceptions, Depression, and Anxiety in Relation to the COVID-19 Outbreak.

BackgroundCOVID-19 has spread rapidly and internationally, which has elicited public panic and psychological problems. Public protective behaviors and perception play crucial roles in controlling the spread of illness and psychological status.MethodsWe conducted a cross-sectional online survey in the hardest-hit Hubei province and other areas in China affected by the COVID-19 outbreak. Questions about their basic information, the perception of the COVID-19 outbreak, recent preventive or avoidance behaviors, and self-reported mental health scales including the Patient Health Questionnaire and Self-Rating Anxiety Scale were included. Binary logistic regressions were used to investigate the association between personal variables/perceptions and psychological distress.ResultsA total of 6261 people were included in the analysis, with 3613 (57.7%) in Hubei province (1743 in Wuhan). The majority of people have adopted preventive and avoidance behaviors. People from Hubei, with contact history, and people who were infected or whose family members were infected had a much higher prevalence of depression and anxiety. Providing truthful and sufficient information, informing the public about the severity of the disease, and perceptions that the outbreak can be controlled by protective behaviors were associated with lower prevalence of depression and anxiety.ConclusionsAssessing the public response, perception, and psychological burden during the outbreak may help improve public health recommendations and deliver timely psychological intervention. Further research may focus on the psychological status of a specialized group to identify methods of delivery of better support based on public response and psychological demand.

Truth Discovery in Big Data Social Media Sensing Applications

The detection of truthful information amid data provided by online social media platforms (e.g., Twitter, Facebook, Instagram) is a critical task in the trend of big data. Truth Discovery is nothing but the extraction of true information or facts from unwanted and raw data, which has become a difficult task nowadays in today's day and age due to the rampant spread of rumors and false information. Before posting anything on the social media platform, people do not consider fact-checking and the source authenticity and frantically spread them by re-posting them which has made the detection of truthful claims more difficult than ever. So, this problem needs to be addressed soon since the impact of false information and misunderstanding can be very powerful and misleading. This mission, truth discovery, is targeted at establishing the authenticity of the sources and therefore the truthfulness of the statements that they create without knowing whether it is true or not. We propose a Big Data Truth Discovery Scheme (BDTD) to overcome the major problems. We have three major problems, the main one being "False information spread" where a large number of sources lead to false or fake statements, making it difficult to distinguish true statements, now this problem is solved by our scheme by studying the various behaviors of sources. On Twitter for example rumormongering is common. The second problem is "lack of claims" where most outlets contribute only a tiny small number of claims, giving very few pieces of evidence and making it not sufficient to analyze the trustworthiness of such sources, this problem is addressed by our scheme where it uses an algorithm that evaluates the claim’s truthfulness and historic contributions of the source regarding the claim. Thirdly the scalability challenge, due to the clustered design of their existing truth discovery algorithms, many existing approaches don't apply to Big-scale social media sensing cases so this challenge is managed by our scheme by making use of frameworks HTCondor and Work Queue. This scheme computes both the reliability of the sources and, ultimately, the legitimacy of statements using a novel approach. A distributed structure is also developed for the implementation of the proposed scheme by making use of the Work Queue (platform) in the HTCondor method (maybe distributed). Findings of the test on a real-world dataset indicate that the BDTD system greatly outperforms the existing methods of Discovery of Truth both in terms of performance and efficiency.

Hipócrates, Agustín y Kepler, fuentes de referencia contra la propagación en internet de las falsas noticias de salud

Noticias falsas, afirmaciones engañosas, bulos, medias verdades, mentiras… Para vencer al virus de la desconfianza, alimentado por las falsas noticias de salud que circulan por internet -y más en tiempos de coronavirus-, la receta es acudir a los expertos, como son los científicos y los profesores, actividad que comparten Hipócrates, Agustín de Hipona y Kepler. Desde nuestro punto de vista, los tres están entre esas fuentes de prestigio para informarse bien de los acontecimientos y descubrir las falsas noticias (fake news) de las que, por regla general, la gente se entera por un tuit o una notificación que salta de pronto en la pantalla de su smartphone. A nuestro entender, las bases de la información veraz no las ofrece la tecnología como un fin en sí mismo. Es el espíritu de las profundas convicciones lo que define la objetividad subyacente en los hechos comprobables.