Misleading Information Research Articles

Women Image in Current Chinese Media and Its Consequences

The negative portrayal of women caused by the male perspective of the media industry needs to be addressed and rectified. In this way, the passive situation of women being otherized can be alleviated, and the misogynistic thinking caused by misleading information conveyed by the media can be improved. It is of great significance to rectify the media under the patriarchal concept to promote gender equality, slow down gender exploitation, reshape female image and enhance female subject consciousness. Mass media unconsciously spread the concept of male supremacy in a subtle way, which not only makes women suffer from the oppression of unequal patriarchal consciousness in the society, but also makes womens cognition of their own gender group and self-image distorted. Reorganizing the media under the patriarchal concept can further promote male equality. Shaping a more objective female media image is of great significance for promoting gender equality, slowing down gender exploitation, reshaping female image and enhancing female subject consciousness.

Impact of Credibility of Online Information on ADHD in the MENA Region

Recent studies have focused on mental and physical disabilities and disorders, particularly in the Arab world. However, there has been a lack of research on online information about attention deficit hyperactivity disorder (ADHD) and the discourse in which this information is presented on websites in the MENA region. This study aims to evaluate the credibility and accuracy of online information about ADHD, and the language discourse in which it is presented, in the Arab world. The study collected 22 landing pages of ADHD websites from ten Arab countries and applied three stages of inductive qualitative and partial quantitative approach: website credibility, website content analysis, and language discourse analysis. The findings showed that few websites(13%) had high credibility, and most did not have credibility elements on their landing page. Majority of the websites(86%) discussed key ADHD issues, but spread misleading information about ADHD. The discourse analysis revealed that some websites(23%) used authoritative discourse (AD), 27% used internal persuasive discourse (IPD), 23% used AD-IPD, and 27% were neutral. These findings suggest that the discourse used to discuss ADHD leans towards the AD where the science voice is dominant. This study highlights the need to improve the credibility, accessibility, and accuracy of online information on health-related topics, particularly for caregivers, parents, and teachers who are the first line spotters of ADHD symptoms in the MENA region.

An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting

The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm.

MPCNet: Compressed multi-view video restoration via motion-parallax complementation network

The performance in restoring compressed multi-view video (MVV) of the existing learning-based methods is limited because they only utilize information of temporally adjacent frames or parallax neighboring views. However, the compression artifacts caused by multi-view coding (MVC) may be related to the reference errors of intra-frame, inter-frame, and inter-view. In this paper, with delicately utilizing the stereo information from both temporal and parallax domains, a motion-parallax complementation network (MPCNet) is proposed to restore the quality of compressed MVV more efficiently. First, we introduce a motion-parallax complementation strategy consisting of a coarse stage and a fine stage. By mutually compensating the feature extracted from multiple domains, useful multi-frame information can be efficiently preserved and aggregated step by step. Second, an attention-based feature filtering and modulation module (AFFM) is proposed, which provides an efficient fusion method for two features by suppressing misleading information. By deploying it in most submodules of the proposed approach, the representational ability of MPCNet can be improved, resulting in a more substantial restoration performance. Experimental results prove the effectiveness of MPCNet by an average increase of 1.978 dB in PSNR, and 0.0282 in MS-SSIM. The BD-rate reduction can reach 47.342% on average. The subjective quality is greatly improved and lots of compression distortions are eliminated. Meanwhile, this work also benefits the accuracy improvement for high-level vision tasks, e.g., mIoU of semantic segmentation and mAP of object detection achieve 0.352 and 51.71, respectively. Quantitative and qualitative analyses demonstrate that MPCNet outperforms state-of-the-art approaches.

Characteristics of the Online Market for Anabolic-Androgenic Steroids in Central Asia: A Netnographic Analysis

Background: With the online proliferation of illegal substances, the Internet offers a wide variety of information on the acquisition and intake of anabolic-androgenic steroids (AAS) and other performance and image enhancing drugs. This study focuses on investigating the characteristics of the online AAS market in Central Asia. Objectives: The primary objectives of this study were to investigate the accessibility and features of the online market for AAS in Central Asia. To achieve this, we employed a netnographic approach for a systematic exploration of websites advertising and selling AAS. The study aimed to conduct a comprehensive analysis of several key aspects, including the variety of AAS products offered, the quality of health advice provided the level of product availability, the procedures involved in making purchases, and the pricing structures within this market. Results: Twenty-one websites supplying AAS in Central Asia met our inclusion criteria. Using content analysis, data were gathered on AAS offerings, quality of health advice provided, availability, purchase process, and prices. Data were synthesized using descriptive statistics. Results indicate that AAS are easily accessible for purchase without valid medical prescription in the Central Asia online market. Most websites advertised the aesthetic and ergogenic benefits of AAS use without indicating the potential complications and adverse effects. Conclusions: Public health efforts to mitigate AAS use in Central Asia should consider both the online accessibility of AAS and the lack of accompanying information on potential complications as well as adverse effects associated with their use. Efforts must be intensified to curtail the proliferation of AAS and related misleading information on the Central Asian online market.

Evaluating the performance of ChatGPT in answering questions related to urolithiasis.

ChatGPT is an artificial intelligence (AI) program with natural language processing. We analyzed ChatGPT's knowledge about urolithiasis whether it can be used to inform patients about urolithiasis. Frequently asked questions (FAQs) about urolithiasis on the websites of urological associations and hospitals were analyzed. Also, strong recommendation-level information was gathered from the urolithiasis section of the European Association of Urology (EAU) 2022 Guidelines. All questions were asked in order in ChatGPT August 3rd version. All answers were evaluated separately by two specialist urologists and scored between 1 and 4, where 1: completely correct, 2: correct but inadequate, 3: a mix of correct and misleading information, and 4: completely incorrect. Of the FAQs, 94.6% were answered completely correctly. No question was answered completely incorrectly. All questions about general, diagnosis, and ureteral stones were graded as 1. Of the 60 questions prepared according to the EAU guideline recommendations, 50 (83.3%) were evaluated as grade 1, and 8 (13.3%) and 2 (3.3%) as grade 3. All questions related to general, diagnostic, renal calculi, ureteral calculi, and metabolic evaluation received the same answer the second time they were asked. Our findings demonstrated that ChatGPT accurately and satisfactorily answered more than 95% of the questions about urolithiasis. We conclude that applying ChatGPT in urology clinics under the supervision of urologists can help patients and their families to have better understanding on urolithiasis diagnosis and treatment.

How Physicians Tackle Internet-Misinformed Patients: Going Beyond Traditional Patient-Centered Communication - A Study Protocol.

The proliferation of misleading and irrelevant health information on the Internet has become a significant public concern. Inappropriate use of online materials can cause harm to patients' health and quality of life. While close attention has been paid to health campaigns and education programs that aim to disseminate accurate health knowledge, the role of physicians, who directly communicate with patients in medical encounters and provide personalized information, has been overlooked. Therefore, this study focuses on physicians and their communication strategies with internet-misinformed patients (IMPs). This study aims to understand the communicative strategies physicians use to tackle IMPs and explore connections between physicians' communicative strategies and patient-centered communication. Approximately 10 to 15 physicians from diverse cultural backgrounds, including Ticino (an Italian-speaking region in Switzerland), Milan and China will be interviewed. Interviews will be conducted in-person or online through video conferencing software programs. Physicians will be asked about their experiences with IMPs, communicative strategies for addressing patients' misconceptions, balancing patient preferences, decision-making obstacles, and envisioning an ideal relationship with them. A thematic analysis will be utilized to analyze data, employing a general inductive approach. The results will provide valuable insights into effective clinical communication strategies that address patients' misuse of internet materials and inform policymakers and healthcare providers about the limitations and applicability of patient-centered communication in the current digital era.

Model and solution to prevent misinformation on social networks

Social networking sites such as Twitter and Facebook have become the primary sources of information dissemination and transmission channels of marketing information. However, in addition to the advantages of Vietnam, online social networks have also been used to spread false information, which can lead to unexpected consequences for the state, the people, and social congress; for example, spreading panic among the public due to the false information spread of swine flu on Twitter in 2009. Therefore, early detection and prevention of misleading information in composite social networks is an urgent need.

Is Artificial Intelligence Intelligent Enough to Read Human Sentiment?

Sentiment analysis is the mining of text which extracts information from the source material and helps a business to understand the social sentiment of their service while monitoring online conversations. Sentiment analysis can apply in any social domain because opinions are central to human activities and prominent influencer of our behaviors. Our beliefs, perceptions of reality, and choices are conditioned by how others evaluate the world. Due to this, when humans need to make a decision, they often seek out the opinions of others. According to GlobalWebIndex, 54% of social media users use media reviews, and 71% are more likely to purchase services based on social media referrals. Therefore, Sentiment analysis is "opinion mining" because it's all about digging into the context of social posts to understand the opinions they reveal. Misleading information has a more substantial effect on society than before. Not only this, but cognitive bias is also systematic errors in thinking, usually inherited by cultural and personal experiences, that can lead to distortions of perceptions when making decisions. Consequently, sentiment analysis can provide you with a broader perspective prior to making a final decision. This research aims to use artificial intelligence techniques for an in-depth analysis of human sentiments. The use cases and dataset used for this research are: 
 
 Stock Analysis using FinViz website
 Climate Change Analysis using Twitter 
 Movie review using IMDB dataset
 
 I used NLTK, Vader and Logistic regression machine-learning models and received approx 80% accuracy which shows us it is very promising that Machine Learning can help understand human sentiments and facilitates informed decision-making.

Fighting the infodemic: the 4 i Framework for Advancing Communication and Trust

BackgroundThe proliferation of false and misleading health claims poses a major threat to public health. This ongoing “infodemic” has prompted numerous organizations to develop tools and approaches to manage the spread of falsehoods and communicate more effectively in an environment of mistrust and misleading information. However, these tools and approaches have not been systematically characterized, limiting their utility. This analysis provides a characterization of the current ecosystem of infodemic management strategies, allowing public health practitioners, communicators, researchers, and policy makers to gain an understanding of the tools at their disposal.MethodsA multi-pronged search strategy was used to identify tools and approaches for combatting health-related misinformation and disinformation. The search strategy included a scoping review of academic literature; a review of gray literature from organizations involved in public health communications and misinformation/disinformation management; and a review of policies and infodemic management approaches from all U.S. state health departments and select local health departments. A team of annotators labelled the main feature(s) of each tool or approach using an iteratively developed list of tags.ResultsWe identified over 350 infodemic management tools and approaches. We introduce the 4 i Framework for Advancing Communication and Trust (4 i FACT), a modified social-ecological model, to characterize different levels of infodemic intervention: informational, individual, interpersonal, and institutional. Information-level strategies included those designed to amplify factual information, fill information voids, debunk false information, track circulating information, and verify, detect, or rate the credibility of information. Individual-level strategies included those designed to enhance information literacy and prebunking/inoculation tools. Strategies at the interpersonal/community level included resources for public health communicators and community engagement approaches. Institutional and structural approaches included resources for journalists and fact checkers, tools for managing academic/scientific literature, resources for infodemic researchers/research, resources for infodemic managers, social media regulation, and policy/legislation.ConclusionsThe 4 i FACT provides a useful way to characterize the current ecosystem of infodemic management strategies. Recognizing the complex and multifaceted nature of the ongoing infodemic, efforts should be taken to utilize and integrate strategies across all four levels of the modified social-ecological model.

The Quality of Coronary Artery Bypass Grafting Videos on YouTube.

Objective YouTube (YouTube LLC,San Bruno, California, United States), one of the most accessed sites on the internet, has become a widespread source of healthcare information for patients. Videos about coronary artery bypass grafts (CABG) have accrued tens of millions of views on the platform, yet their educational quality is unknown. This study investigates the educational landscape of videos regarding CABG procedures on YouTube. Methods YouTube was queried for "CoronaryArteryBypass Graft Surgery" and "CoronaryArteryBypass Graft Procedure". After applying exclusion criteria, 73 videos were assessed. Two independent reviewers rated the material with the Global Quality Scale (GQS) (5 = high quality, 0 = low quality) to judge educational value. A ratio of view count to days since upload was applied to assess video popularity. Source, modality, and date of upload were recorded for each video as well. Results An average GQS score of 2.94 was found, indicating poor educational quality of the 73 YouTube videos on CABG procedures. Videos uploaded by physicians (56/73; 76.7%) had a significantly higher average GQS score than those uploaded by non-physicians (p<0.001). When content was grouped by delivery method, physician-led presentations (24/73 or 32.9%) produced the highest average GQS score of 3.35; conversely, patient-friendly delivery methods (18/73 or 24.7%) yielded the lowest average GQS score of 2.36 (p<0.001). Neither the view ratio nor the days since upload significantly correlated with the educational quality of the video. Conclusion Although CABG videos are readily available on YouTube, they often contain considerable biases and misleading information. With online sources for healthcare education now commonplace, physicians must be aware of the vast quantities of low-quality videos patients often encounter when weighing different treatment options. Further analysis of CABG videos on YouTube may allow physicians to ameliorate this gap by producing videos that are not only high quality but highly viewed on the platform.

Rampant online marketing of teeth whitening products: Evaluation of online information, labelling accuracy and quantitative analysis of high peroxide content gels

Background/Purpose: Online vendors seize the advantage of the high demand on home-use, do-it-yourself dental bleaching products. The study aims to present the uncontrolled online market of dental products and provide evidence of consumer safety risks associated with the utilization of high peroxide content bleaching products without dentist's supervision, and also to identify misleading and insufficient information on content and compromised product quality. Materials and methodsA complex risk-based methodology was used including website content evaluation focusing on ingredients, precautionary statements and directions for use provided by online retailers. Bleaching products were test procured in which packaging and labels were documented and assessed. Quality control was performed using the permanganometric method per the official European Pharmacopoeia. ResultsOne (16.7%) of six test procured peroxide gels was not delivered. Another arrived without enclosed description or instructions. The ingredient list was incomplete or missing for all (100%) online products, however, it was listed on the label or in enclosed documentation in four out of five (80%) samples. Precautionary statements were scarcely (16.7%) disclosed online, contrarily, safety claims were emphasized by most (83.3%) websites. Contraindications and adverse effects were mentioned in the majority (80%) of the delivered product labels. One sample contained no active principle, in two sample’ peroxide content exceeded the label's claim by 5.2–9.0% while in another two it was below the concentration indicated on the labels by 79.9–80.7%. ConclusionsDissimilarity in regulations elicits an opportunity for consumers to purchase inappropriately labeled, questionable quality, high peroxide content dental products without information regarding ingredients, application and risks. The uncontrolled market, easy access and unsupervised application of high peroxide-content teeth whiteners imply patient safety issues.

Satisfaction Status of Pharmacists Towards Pharmacy Information Systems: A Qualitative Study

This study aimed to qualitatively assess the satisfaction of pharmacists in Turkey regarding the use of Pharmacy Information Systems (PIS). Conducted in Zeytinburnu, Istanbul, 15 pharmacies were included in the sample. Pharmacists or pharmacy owners voluntarily participated, answering pre-set questions about their satisfaction and challenges with PIS. Key findings revealed pharmacists' satisfaction revolved around the system's speed, ease of use, practicality, and functionality. However, 20% of participants could not pinpoint any satisfactory aspects, indicating improvements are necessary for user experience. Most strikingly, problems related to the Social Security Institution (SGK) system emerged as critical, with 46.66% expressing dissatisfaction and 40% stating unjust penalization from the SGK. System update processes and slow system speed were other significant issues. The study established the need for improvements that enhance user experience and system speed, resolving issues related to the Social Security Institution (SGK) integration, making electronic information systems more functional, making stock control and medicine tracking systems more effective, preventing erroneous or misleading information in the system, and organizing training and support programs to enable pharmacists to use the system more efficiently.

Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies.

Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms. This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19-related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality. A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants' feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users. A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3). Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19-related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19-related misinformation on social media.

Combating Scientific Disinformation on Gender-Affirming Care.

Scientific disinformation is false and misleading information that is used intentionally by legal and political actors to sway public opinion and oppose facts. In recent years, disinformation has become a tool for authorities to limit gender-affirming health care (GAC) for transgender and gender-expansive youth who experience gender dysphoria. Existing modes of expert intervention in health policy may not be sufficient to match the pace of these quickly unfolding health care bans. A cross-disciplinary team of academics in medicine, psychology, and law assembled to challenge scientific disinformation on GAC with 2 rapid-response rebuttal reports. Reports were produced in 3 to 10 weeks after the passage of GAC bans in Texas, Alabama, and Florida in 2022. They were posted online to facilitate dissemination and engage litigators, judges, policy experts, advocates, parents, and others. The team's efforts complemented public statements by medical societies and lawsuits brought by national LGBTQ litigators. The team's reports were cited in legal challenges to GAC bans in Texas, Alabama, and Florida. The team also filed amicus briefs for direct consideration by the courts and public comments to health care agencies in Florida. The reports received coverage in local and national media outlets in broadcast and print media. This advocacy case study describes the process used to challenge disinformation about GAC with rapid-response rebuttal reports, as well as the impact of this work and associated challenges. In an increasingly polarized political climate, this process may be adapted to other areas of health policy in which scientific disinformation takes root.

XSC—An eXplainable Image Segmentation and Classification Framework: A Case Study on Skin Cancer

Within the field of computer vision, image segmentation and classification serve as crucial tasks, involving the automatic categorization of images into predefined groups or classes, respectively. In this work, we propose a framework designed for simultaneously addressing segmentation and classification tasks in image-processing contexts. The proposed framework is composed of three main modules and focuses on providing transparency, interpretability, and explainability in its operations. The first two modules are used to partition the input image into regions of interest, allowing the automatic and interpretable identification of segmentation regions using clustering techniques. These segmentation regions are then analyzed to select those considered valuable by the user for addressing the classification task. The third module focuses on classification, using an explainable classifier, which relies on hand-crafted transparent features extracted from the selected segmentation regions. By leveraging only the selected informative regions, the classification model is made more reliable and less susceptible to misleading information. The proposed framework’s effectiveness was evaluated in a case study on skin-cancer-segmentation and -classification benchmarks. The experimental analysis highlighted that the proposed framework exhibited comparable performance with the state-of-the-art deep-learning approaches, which implies its efficiency, considering the fact that the proposed approach is also interpretable and explainable.

A further look at the nano/micro-indentation method for measuring and ranking Young’s modulus and hardness of materials

Determination of the intrinsic Young’s modulus (E) is essential for material design and applications. However, the commonly used micro/nano-indentation method does not give accurate intrinsic Young’s modulus, since the measured modulus comes from the damaged zone under the indent tip. In this study, we analyze the intrinsic Young’s modulus or that without local damage caused by indentation, and determine that the intrinsic Young’s modulus can be determined by extrapolation of the E ∼ load curve as the indentation load approaches zero. To support this finding, indentation behaviors of five ceramic materials (Al2O3, Si3N4, ZrO2, glass and cemented WC/Co) were analyzed and compared with those determined using an acoustic method. The intrinsic Young’s modulus measured, e.g., using the acoustic method, are appropriate for material ranking, while Young’s moduli of different materials measured by indentation under the same load could give misleading information because of different degrees of local damage to the materials under the indenter. Underlying mechanisms for the observed phenomena shown in this novel and unique study are elucidated based on the interatomic bonding. Hardness versus load curves show trends similar to those of Young’s modulus. However, unlike the Young’s modulus, the hardness values measured under the same load can be directly used to rank materials; the reason behind is also discussed.

Global Guided Cross-Modal Cross-Scale Network for RGB-D Salient Object Detection.

RGB-D saliency detection aims to accurately localize salient regions using the complementary information of a depth map. Global contexts carried by the deep layer are key to salient objection detection, but they are diluted when transferred to shallower layers. Besides, depth maps may contain misleading information due to the depth sensors. To tackle these issues, in this paper, we propose a new cross-modal cross-scale network for RGB-D salient object detection, where the global context information provides global guidance to boost performance in complex scenarios. First, we introduce a global guided cross-modal and cross-scale module named G2CMCSM to realize global guided cross-modal cross-scale fusion. Then, we employ feature refinement modules for progressive refinement in a coarse-to-fine manner. In addition, we adopt a hybrid loss function to supervise the training of G2CMCSNet over different scales. With all these modules working together, G2CMCSNet effectively enhances both salient object details and salient object localization. Extensive experiments on challenging benchmark datasets demonstrate that our G2CMCSNet outperforms existing state-of-the-art methods.

Hysteroscopy in the new media: quality and reliability analysis of hysteroscopy procedures on YouTube™

BackgroundHysteroscopy plays a crucial role in diagnosing and managing various intrauterine pathologies. However, its execution can be influenced by patients’ perception and understanding, which are often shaped by digital resources such as YouTubeTM. Given its popularity and accessibility, YouTubeTM has the potential to greatly influence patients’ knowledge and expectations about this procedure, highlighting the need for accurate and reliable information.PurposeThis study aims to assess the reliability and quality of hysteroscopy information available to patients on YouTubeTM. Understanding the nature of information patients’ access can help address their fears and potential misunderstandings about the procedure, consequently reducing the likelihood of suspension or postponement due to anxiety.MethodsA comprehensive analysis of YouTubeTM was conducted, simulating the search process of a patient seeking information about hysteroscopy. The study evaluated the reliability and quality of 90 out of the first 100 hysteroscopy-related videos on YouTubeTM, scored by four gynecologists—two experienced hysteroscopists and two trainees. The videos were assessed for reliability and quality using the mDISCERN and Global Quality Scale (GQS) scores.ResultsThe average mDISCERN and GQS scores for the evaluated videos were below the optimal three points, highlighting the lack of fluency, comprehensiveness, and reliability of the available information. Notably, while videos produced by experts, including doctors and professional channels, had higher scores, they still fell short of the minimum score of 3. These videos also were not considered more suitable for either patients or trainees. Videos that were assessed as reliable (mDISCERN ≥ 3) were observed to be longer and were more frequently produced by doctors. These videos were suggested more to trainees rather than patients. Similarly, videos deemed as fluent and comprehensive (GQS ≥ 3) were longer and were more often recommended to patients.ConclusionsWhile YouTubeTM is a widely used source of medical information, the quality and reliability of hysteroscopy videos on the platform are poor. The strategic use of selected, high-quality hysteroscopy videos can enhance procedure success and alleviate patient fears. However, the unsupervised discovery of information by patients could potentially lead to procedure failure or an elevated level of stress due to misleading or incorrect information.

Efforts to Improve the Accuracy of Information About Electroconvulsive Therapy Given to Patients and Families

Objective:Many thousands of people still receive electroconvulsive therapy but it remains highly contested. A recent audit of the United Kingdom patient information leaflets found multiple inaccuracies and omissions, minimizing risks and exaggerating benefits (e.g., only six leaflets mentioned cardiovascular events). This study reports efforts to improve accuracy for patients and families.Methods:Letters were sent twice to managers of all 51 United Kingdom National Health Service trusts (regional bodies which deliver most health care) detailing the audit’s findings and the accuracy of their own trust’s leaflet, also asking what changes would be undertaken.Results:Only nine trusts responded and three committed to improvements. The Royal College of Psychiatrists released a slightly better but still highly misleading information sheet. Efforts to engage government and all other relevant United Kingdom bodies failed.Conclusions:Trusts are unwilling to correct misinformation/ensure balanced information.