TikTok content on measles-rubella vaccine in Jordan: A cross-sectional study highlighting the spread of vaccine misinformation.

Social media platforms, including TikTok, have become influential sources of health information. However, they also present as potential sources for the spread of vaccine misinformation. The aim of this study was to assess the quality of measles-rubella (MR) vaccine-related contents on TikTok in Jordan and to analyze factors associated with vaccine misinformation. A systematic search for MR vaccine-related TikTok contents in Jordan was conducted using pre-defined keywords and a specified time range. Content metrics (likes, comments, shares, and saves) were collected while the content quality of health information was evaluated using a modified version of the DISCERN, a validated instrument by two expert raters. The average modified DISCERN score ranged from 1, denoting poor content, to 5, indicating excellent content. A total of 50 videos from 34 unique content creators formed the final study sample. The majority of MR vaccine-related content was created by lay individuals (61.8%), followed by TV/news websites/journalists (23.5%), and healthcare professionals (HCPs) (14.7%). The Cohen κ per modified DISCERN item was in the range of 0.579-0.808, p<0.001), indicating good to excellent agreement. The overall average modified DISCERN score was 2±1.2, while it was only 1.3±0.52 for lay individuals' content, which indicated poor content quality. For the normalized per number of followers for each source, content by lay individuals had a significantly higher number of likes, saves, and shares with p=0.009, 0.012, and 0.004, respectively. Vaccine misinformation was detected in 58.8% of the videos as follows: lay individuals (85.7%), TV/news websites/journalists (25.0%), and HCPs content had none (p<0.001). Normalized per the number of followers for each source, videos flagged as having MR vaccine misinformation reached a higher number of likes, saves, and shares (p=0.012, 0.016, and 0.003, respectively). In conclusion, substantial dissemination of TikTok MR vaccine-related misinformation in Jordan was detected. Rigorous fact-checking is warranted by the platform to address misinformation on TikTok, which is vital to improve trust in MR vaccination and ultimately protect public health.

Bibliometric top ten healthcare-related ChatGPT publications in the first ChatGPT anniversary.

Since its public release on November 30, 2022, ChatGPT has shown promising potential in diverse healthcare applications despite ethical challenges, privacy issues, and possible biases. The aim of this study was to identify and assess the most influential publications in the field of ChatGPT utility in healthcare using bibliometric analysis. The study employed an advanced search on three databases, Scopus, Web of Science, and Google Scholar, to identify ChatGPT-related records in healthcare education, research, and practice between November 27 and 30, 2023. The ranking was based on the retrieved citation count in each database. The additional alternative metrics that were evaluated included (1) Semantic Scholar highly influential citations, (2) PlumX captures, (3) PlumX mentions, (4) PlumX social media and (5) Altmetric Attention Scores (AASs). A total of 22 unique records published in 17 different scientific journals from 14 different publishers were identified in the three databases. Only two publications were in the top 10 list across the three databases. Variable publication types were identified, with the most common being editorial/commentary publications (n=8/22, 36.4%). Nine of the 22 records had corresponding authors affiliated with institutions in the United States (40.9%). The range of citation count varied per database, with the highest range identified in Google Scholar (1019-121), followed by Scopus (242-88), and Web of Science (171-23). Google Scholar citations were correlated significantly with the following metrics: Semantic Scholar highly influential citations (Spearman's correlation coefficient ρ=0.840, p<0.001), PlumX captures (ρ=0.831, p<0.001), PlumX mentions (ρ=0.609, p=0.004), and AASs (ρ=0.542, p=0.009). In conclusion, despite several acknowledged limitations, this study showed the evolving landscape of ChatGPT utility in healthcare. There is an urgent need for collaborative initiatives by all stakeholders involved to establish guidelines for ethical, transparent, and responsible use of ChatGPT in healthcare. The study revealed the correlation between citations and alternative metrics, highlighting its usefulness as a supplement to gauge the impact of publications, even in a rapidly growing research field.

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5 and Humans in Clinical Chemistry Multiple-Choice Questions.

Introduction: Artificial intelligence (AI) chatbots excel in language understanding and generation. These models can transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. This study aimed to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard compared to human students at a postgraduate master's level in Medical Laboratory Sciences. Methods: The study design was based on the METRICS checklist for the design and reporting of AI-based studies in healthcare. The study utilized a dataset of 60 Clinical Chemistry multiple-choice questions (MCQs) initially conceived for assessing 20 MSc students. The revised Bloom's taxonomy was used as the framework for classifying the MCQs into four cognitive categories: Remember, Understand, Analyze, and Apply. A modified version of the CLEAR tool was used for the assessment of the quality of AI-generated content, with Cohen's κ for inter-rater agreement. Results: Compared to the mean students' score which was 0.68±0.23, GPT-4 scored 0.90 ± 0.30, followed by Bing (0.77 ± 0.43), GPT-3.5 (0.73 ± 0.45), and Bard (0.67 ± 0.48). Statistically significant better performance was noted in lower cognitive domains (Remember and Understand) in GPT-3.5 (P=0.041), GPT-4 (P=0.003), and Bard (P=0.017) compared to the higher cognitive domains (Apply and Analyze). The CLEAR scores indicated that ChatGPT-4 performance was "Excellent" compared to the "Above average" performance of ChatGPT-3.5, Bing, and Bard. Discussion: The findings indicated that ChatGPT-4 excelled in the Clinical Chemistry exam, while ChatGPT-3.5, Bing, and Bard were above average. Given that the MCQs were directed to postgraduate students with a high degree of specialization, the performance of these AI chatbots was remarkable. Due to the risk of academic dishonesty and possible dependence on these AI models, the appropriateness of MCQs as an assessment tool in higher education should be re-evaluated.

The performance of OpenAI ChatGPT-4 and Google Gemini in virology multiple-choice questions: a comparative analysis of English and Arabic responses.

OBJECTIVE: The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency of generative AI in different languages to answer complex questions is crucial for educational purposes. The study objective was to compare the performance ChatGPT-4 and Gemini in answering Virology multiple-choice questions (MCQs) in English and Arabic, while assessing the quality of the generated content. Both AI models' responses to 40 Virology MCQs were assessed for correctness and quality based on the CLEAR tool designed for evaluation of AI-generated content. The MCQs were classified into lower and higher cognitive categories based on the revised Bloom's taxonomy. The study design considered the METRICS checklist for the design and reporting of generative AI-based studies in healthcare. RESULTS: ChatGPT-4 and Gemini performed better in English compared to Arabic, with ChatGPT-4 consistently surpassing Gemini in correctness and CLEAR scores. ChatGPT-4 led Gemini with 80% vs. 62.5% correctness in English compared to 65% vs. 55% in Arabic. For both AI models, superior performance in lower cognitive domains was reported. Both ChatGPT-4 and Gemini exhibited potential in educational applications; nevertheless, their performance varied across languages highlighting the importance of continued development to ensure the effective AI integration in healthcare education globally.

The Worrying Phenomenon of COVID-19 Vaccine Hesitancy and Its Negative Impact on Pandemic Control Efforts: Common Themes that Emerged in the Middle East and North Africa (MENA) Region.

Since the declaration of coronavirus disease 2019 (COVID-19) as a pandemic, intensive measures were taken to mitigate its negative health, psychological, social, and economic impact. COVID-19 continues to pose serious threats globally, with vaccination as the central safe strategy to control the pandemic. However, COVID-19 vaccine hesitancy is a major concern, especially in the Middle East and North Africa (MENA). Concerns regarding vaccine safety, efficacy, and misinformation contribute to vaccine hesitancy. Addressing these concerns and providing accurate information is crucial for increasing COVID-19 vaccine acceptance and uptake in this region, where the coverage is low. Variable rates of COVID-19 vaccine hesitancy were found in the numerous studies conducted in the region. Complex factors contributed to vaccination hesitancy in the region including concerns about COVID-19 vaccine safety and efficacy, low trust in healthcare systems, complacency toward the risks of COVID-19, constraints hindering access to COVID-19 vaccination services, as well as the circulation of misinformation and conspiracy beliefs about COVID-19 and its vaccination. Effective approaches to address COVID-19 vaccine hesitancy in the MENA region rely on developing evidence-based communication strategies that are recommended to build trust in vaccination, highlight the disease risks, and counter COVID-19 vaccine-related misinformation. Ensuring COVID-19 vaccine affordability is also necessary besides the cautious consideration of implementing COVID-19 vaccine mandates. Based on the preceding discussion, this chapter aims to identify the common themes of COVID-19 vaccine hesitancy in the MENA region. In addition, the chapter highlights the importance of understanding the root causes of COVID-19 vaccination hesitancy and its associated determinants to develop effective strategies for promoting COVID-19 vaccine acceptance and uptake in the MENA region. To build community trust, promote community education and awareness, and counter misinformation for better COVID-19 vaccine coverage in the region, it is recommended to involve healthcare professionals and policymakers.

Attitude towards cholera vaccination and its related factors in Jordan amid the 2022 Middle East outbreak.

BACKGROUND: An outbreak of cholera was reported in the Middle East by the second half of 2022. Raising public awareness and vaccination against cholera represent critical factors in the preventive efforts. The current study aimed to assess the knowledge of cholera and attitude towards its vaccination among a sample of the general public residing in Jordan. METHODS: An online self-administered questionnaire was distributed to the residents in Jordan using a snowball convenience-based sampling approach. The questionnaire based on previously published studies included items to evaluate sociodemographic variables, knowledge about cholera symptoms, transmission, and prevention and the willingness to accept cholera vaccination. Additionally, four items based on the validated 5 C scale in Arabic were included to assess the psychological factors influencing attitude to cholera vaccination. RESULTS: The final study sample comprised 1339 respondents, of whom 1216 (90.8%) heard of cholera before the study. Among those who heard of cholera, and on a scale from 0 to 20, the overall mean cholera Knowledge score (K-score) was 12.9 ± 3.8. In multivariate analysis, being over 30 years old and occupation as healthcare workers or students in healthcare-related colleges were significantly associated with a higher K-score compared to younger individuals and students in non-healthcare-related colleges. Overall, the acceptance of cholera vaccination if cases are recorded in Jordan, and if the vaccine is safe, effective, and provided freely was reported among 842 participants (69.2%), while 253 participants were hesitant (20.8%) and 121 participants were resistant (10.0%). In linear regression, the significant predictors of cholera vaccine acceptance were solely the three psychological factors namely high confidence, low constraints, and high collective responsibility. CONCLUSIONS: In this study, the identified gaps in cholera knowledge emphasize the need to enhance educational initiatives. Although cholera vaccine acceptance was relatively high, a significant minority of the respondents exhibited vaccination hesitancy or resistance. The evident correlation between the psychological determinants and attitudes toward cholera vaccination emphasizes the need to consider these factors upon designing public health campaigns aimed at cholera prevention. The insights of the current study highlight the importance of addressing both knowledge gaps and psychological barriers to optimize cholera control strategies.

Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic.

BACKGROUND: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. METHODS: The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. RESULTS: In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P = .002). CONCLUSIONS: Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.

An Increase in the Prevalence of Clinically Relevant Resistance-Associated Substitutions in Four Direct-Acting Antiviral Regimens: A Study Using GenBank HCV Sequences.

Direct-acting antivirals (DAAs) revolutionized the therapeutics of chronic hepatitis C. The emergence and transmission of HCV variants with resistance-associated substitutions (RASs) can undermine HCV treatment. This study aimed to assess the prevalence and temporal trends of RASs in HCV, with a particular focus on clinically relevant RASs (cr-RASs). Near-complete HCV GenBank sequences archived in the Los Alamos HCV Database were analyzed. The study period was divided into two phases: before 2011 and from 2011 onward. Identification of RASs across three DAA classes (NS3, NS5A, and NS5B inhibitors) was based on the 2020 EASL guidelines. The AASLD-IDSA recommendations were used to identify cr-RASs for three HCV genotypes/subtypes (1a, 1b, and 3) and four DAA regimens: ledipasvir/sofosbuvir; elbasvir/grazoprevir; sofosbuvir/velpatasvir; and glecaprevir/pibrentasvir. The final HCV dataset comprised 3443 sequences, and the prevalence of RASs was 50.4%, 60.2%, and 25.3% in NS3, NS5A, and NS5B, respectively. In subtype 1a, resistance to ledipasvir/sofosbuvir was 32.8%, while resistance to elbasvir/grazoprevir was 33.0%. For genotype 3, resistance to sofosbuvir/velpatasvir and glecaprevir/pibrentasvir was 4.2% and 24.9%, respectively. A significant increase in cr-RASs was observed across the two study phases as follows: for ledipasvir/sofosbuvir in subtype 1a, cr-RASs increased from 30.2% to 35.8% (p = 0.019); for elbasvir/grazoprevir in subtype 1a, cr-RASs increased from 30.4% to 36.1% (p = 0.018); In subtype 1b, neither ledipasvir/sofosbuvir nor elbasvir/grazoprevir showed any cr-RASs in the first phase, but both were present at a prevalence of 6.5% in the second phase (p < 0.001); for sofosbuvir/velpatasvir in genotype 3, cr-RASs increased from 0.9% to 5.2% (p = 0.006); and for glecaprevir/pibrentasvir, cr-RASs increased from 12.0% to 29.1% (p < 0.001). The rising prevalence of HCV RASs and cr-RASs was discernible. This highlights the necessity for ongoing surveillance and adaptation of novel therapeutics to manage HCV resistance effectively. Updating the clinical guidelines and treatment regimens is recommended to counteract the evolving HCV resistance to DAAs.

The negative impact of misinformation and vaccine conspiracy on COVID-19 vaccine uptake and attitudes among the general public in Iraq.

Background: Vaccine hesitancy is a major barrier to infectious disease control. Previous studies showed high rates of COVID-19 vaccine hesitancy in the Middle East. The current study aimed to investigate the attitudes towards COVID-19 vaccination and COVID-19 vaccine uptake among adult population in Iraq. Methods: This self-administered survey-based study was conducted in August-September 2022. The survey instrument assessed participants' demographics, attitudes to COVID-19 vaccination, beliefs in COVID-19 misinformation, vaccine conspiracy beliefs, and sources of information regarding the vaccine. Results: The study sample comprised a total of 2544 individuals, with the majority reporting the uptake of at least one dose of COVID-19 vaccination (n = 2226, 87.5 %). Positive attitudes towards COVID-19 vaccination were expressed by the majority of participants (n = 1966, 77.3 %), while neutral and negative attitudes were expressed by 345 (13.6 %) and 233 (9.2 %) participants, respectively. Factors associated with positive attitudes towards COVID-19 vaccination in multivariate analysis included disbelief in COVID-19 misinformation and disagreement with vaccine conspiracies. Higher COVID-19 vaccine uptake was significantly associated with previous history of COVID-19 infection, higher income, residence outside the Capital, disbelief in COVID-19 misinformation, disagreement with vaccine conspiracies, and reliance on reputable information sources. Conclusion: COVID-19 vaccine coverage was high among the participants, with a majority having positive attitudes towards COVID-19 vaccination. Disbelief in COVID-19 misinformation and disagreement with vaccine conspiracies were correlated with positive vaccine attitudes and higher vaccine uptake. These insights can inform targeted interventions to enhance vaccination campaigns.

Contemporary Insights into Hepatitis C Virus: A Comprehensive Review.

Hepatitis C virus (HCV) remains a significant global health challenge. Approximately 50 million people were living with chronic hepatitis C based on the World Health Organization as of 2024, contributing extensively to global morbidity and mortality. The advent and approval of several direct-acting antiviral (DAA) regimens significantly improved HCV treatment, offering potentially high rates of cure for chronic hepatitis C. However, the promising aim of eventual HCV eradication remains challenging. Key challenges include the variability in DAA access across different regions, slightly variable response rates to DAAs across diverse patient populations and HCV genotypes/subtypes, and the emergence of resistance-associated substitutions (RASs), potentially conferring resistance to DAAs. Therefore, periodic reassessment of current HCV knowledge is needed. An up-to-date review on HCV is also necessitated based on the observed shifts in HCV epidemiological trends, continuous development and approval of therapeutic strategies, and changes in public health policies. Thus, the current comprehensive review aimed to integrate the latest knowledge on the epidemiology, pathophysiology, diagnostic approaches, treatment options and preventive strategies for HCV, with a particular focus on the current challenges associated with RASs and ongoing efforts in vaccine development. This review sought to provide healthcare professionals, researchers, and policymakers with the necessary insights to address the HCV burden more effectively. We aimed to highlight the progress made in managing and preventing HCV infection and to highlight the persistent barriers challenging the prevention of HCV infection. The overarching goal was to align with global health objectives towards reducing the burden of chronic hepatitis, aiming for its eventual elimination as a public health threat by 2030.

A multinational study on the factors influencing university students' attitudes and usage of ChatGPT.

Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.

A Preliminary Checklist (METRICS) to Standardize the Design and Reporting of Studies on Generative Artificial Intelligence-Based Models in Health Care Education and Practice: Development Study Involving a Literature Review.

BACKGROUND: Adherence to evidence-based practice is indispensable in health care. Recently, the utility of generative artificial intelligence (AI) models in health care has been evaluated extensively. However, the lack of consensus guidelines on the design and reporting of findings of these studies poses a challenge for the interpretation and synthesis of evidence. OBJECTIVE: This study aimed to develop a preliminary checklist to standardize the reporting of generative AI-based studies in health care education and practice. METHODS: A literature review was conducted in Scopus, PubMed, and Google Scholar. Published records with "ChatGPT," "Bing," or "Bard" in the title were retrieved. Careful examination of the methodologies employed in the included records was conducted to identify the common pertinent themes and the possible gaps in reporting. A panel discussion was held to establish a unified and thorough checklist for the reporting of AI studies in health care. The finalized checklist was used to evaluate the included records by 2 independent raters. Cohen κ was used as the method to evaluate the interrater reliability. RESULTS: The final data set that formed the basis for pertinent theme identification and analysis comprised a total of 34 records. The finalized checklist included 9 pertinent themes collectively referred to as METRICS (Model, Evaluation, Timing, Range/Randomization, Individual factors, Count, and Specificity of prompts and language). Their details are as follows: (1) Model used and its exact settings; (2) Evaluation approach for the generated content; (3) Timing of testing the model; (4) Transparency of the data source; (5) Range of tested topics; (6) Randomization of selecting the queries; (7) Individual factors in selecting the queries and interrater reliability; (8) Count of queries executed to test the model; and (9) Specificity of the prompts and language used. The overall mean METRICS score was 3.0 (SD 0.58). The tested METRICS score was acceptable, with the range of Cohen κ of 0.558 to 0.962 (P<.001 for the 9 tested items). With classification per item, the highest average METRICS score was recorded for the "Model" item, followed by the "Specificity" item, while the lowest scores were recorded for the "Randomization" item (classified as suboptimal) and "Individual factors" item (classified as satisfactory). CONCLUSIONS: The METRICS checklist can facilitate the design of studies guiding researchers toward best practices in reporting results. The findings highlight the need for standardized reporting algorithms for generative AI-based studies in health care, considering the variability observed in methodologies and reporting. The proposed METRICS checklist could be a preliminary helpful base to establish a universally accepted approach to standardize the design and reporting of generative AI-based studies in health care, which is a swiftly evolving research topic.

Compulsory Vaccination Coverage in 12 Sub-Saharan African Countries Two Years Following the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic is a global threat, challenging health services' provision and utilization. This study aimed to assess compulsory vaccination coverage in 12 Sub-Saharan African countries two years following the COVID-19 pandemic using the Health Belief Model. A cross-sectional survey was conducted from November 1 to December 15, 2022. Multivariate logistic regression was conducted to identify the determinants of vaccination coverage. Among the 5032 respondents, 73.1% reported that their children received compulsory vaccination. The lowest coverage was observed in Ghana (36.5%), while the highest was in Burkina Faso and Congo (92.0%). Factors associated with non-vaccination included older mothers (adjusted odds ratio (AOR) = 1.04, 95%CI: 1.03-1.05), lower mothers' education, older children (AOR = 0.76, 95%CI: 0.60-0.96), children with chronic illnesses (AOR = 0.55, 95%CI: 0.45-0.66), and difficult accessibility to healthcare facilities (AOR = 11.27, 95%CI: 9.48-13.44). Low perceived risk, in which non-vaccinated children were believed to be at no higher risk for infectious diseases and the disease severity would not worsen among non-vaccinated children, increased the likelihood of non-vaccination (AOR = 2.29, 95%CI: 1.75-2.99 and AOR = 2.12, 95%CI: 1.64-2.73, respectively). Perceiving vaccines as unnecessary, and needless for breastfed babies increased the probability of non-vaccination (AOR = 1.38, 95%CI: 1.10-1.73 and AOR = 1.69, 95%CI: 1.31-2.19, respectively). Higher odds of non-vaccination were found when the provision of vaccine information did not motivate parents to vaccinate their children (AOR = 4.29, 95%CI: 3.15-5.85). Conversely, believing that vaccines were safe for children decreased the odds of non-vaccination (AOR = 0.72, 95%CI: 0.58-0.88). Parental perceptions and concerns should be considered in interventions aiming to increase compulsory vaccine acceptance and coverage.

Rating communication skills in dental practice: the impact of different sociodemographic factors.

BACKGROUND: Communication abilities are essential for the successful operation of a dental business and significantly influence outcomes, compliance, and patient satisfaction. AIMS AND METHODS: The aim of our study was to evaluate the knowledge and practice of doctor-patient communication among Jordanian dentists. This evaluation was conducted through a survey based on the key components of the Calgary Cambridge Observation Guides. Additionally, the impact of several sociodemographic characteristics on communication abilities was investigated. This cross-sectional study was conducted from January to June 2022. The data collection tool was an online questionnaire developed by the researchers, consisting of three sections: self-reported demographic and professional data, the practice of doctor-patient communication, and knowledge of doctor-patient communication. RESULTS: The study included 305 dentists, comprising 106 males and 199 females, with a mean age of 32.9 ± 9.0 years. The mean score for communication skills knowledge was 41.5, indicating a moderate level of communication skills knowledge. Female dentists demonstrated significantly higher communication scores compared to their male counterparts, and those working in the private sector scored significantly higher than those in the governmental sector or in both sectors (P ≤ 0.05). In general, older and more experienced dentists exhibited better communication skills. Educational level had a positive impact on certain communication skills items. 58.4% believed that communication skills can always be developed and improved through training sessions, while 48.9% reported never having attended such courses. 95.1% believed that training courses on communication skills are always necessary as part of the educational curriculum. The main obstacles that may deter dentists from considering communication skills courses were limited time (62.3%), course availability (37.7%), cost (28.2%), and perceived lack of importance (8.2%). CONCLUSION: Among a sample of Jordanian dentists, there appears to be a discrepancy between knowledge and self-reported practices regarding communication abilities. In certain crucial, evidence-based areas of doctor-patient communication, there are fundamental deficiencies. Considering the significant role dentists play in oral health and prevention, communication skills should be a top educational priority for them.

ChatGPT Performance in Diagnostic Clinical Microbiology Laboratory-Oriented Case Scenarios.

BACKGROUND: Artificial intelligence (AI)-based tools can reshape healthcare practice. This includes ChatGPT which is considered among the most popular AI-based conversational models. Nevertheless, the performance of different versions of ChatGPT needs further evaluation in different settings to assess its reliability and credibility in various healthcare-related tasks. Therefore, the current study aimed to assess the performance of the freely available ChatGPT-3.5 and the paid version ChatGPT-4 in 10 different diagnostic clinical microbiology case scenarios. METHODS: The current study followed the METRICS (Model, Evaluation, Timing/Transparency, Range/Randomization, Individual factors, Count, Specificity of the prompts/language) checklist for standardization of the design and reporting of AI-based studies in healthcare. The models tested on December 3, 2023 included ChatGPT-3.5 and ChatGPT-4 and the evaluation of the ChatGPT-generated content was based on the CLEAR tool (Completeness, Lack of false information, Evidence support, Appropriateness, and Relevance) assessed on a 5-point Likert scale with a range of the CLEAR scores of 1-5. ChatGPT output was evaluated by two raters independently and the inter-rater agreement was based on the Cohen's κ statistic. Ten diagnostic clinical microbiology laboratory case scenarios were created in the English language by three microbiologists at diverse levels of expertise following an internal discussion of common cases observed in Jordan. The range of topics included bacteriology, mycology, parasitology, and virology cases. Specific prompts were tailored based on the CLEAR tool and a new session was selected following prompting each case scenario. RESULTS: The Cohen's κ values for the five CLEAR items were 0.351-0.737 for ChatGPT-3.5 and 0.294-0.701 for ChatGPT-4 indicating fair to good agreement and suitability for analysis. Based on the average CLEAR scores, ChatGPT-4 outperformed ChatGPT-3.5 (mean: 2.64±1.06 vs. 3.21±1.05, P=.012, t-test). The performance of each model varied based on the CLEAR items, with the lowest performance for the "Relevance" item (2.15±0.71 for ChatGPT-3.5 and 2.65±1.16 for ChatGPT-4). A statistically significant difference upon assessing the performance per each CLEAR item was only seen in ChatGPT-4 with the best performance in "Completeness", "Lack of false information", and "Evidence support" (P=0.043). The lowest level of performance for both models was observed with antimicrobial susceptibility testing (AST) queries while the highest level of performance was seen in bacterial and mycologic identification. CONCLUSIONS: Assessment of ChatGPT performance across different diagnostic clinical microbiology case scenarios showed that ChatGPT-4 outperformed ChatGPT-3.5. The performance of ChatGPT demonstrated noticeable variability depending on the specific topic evaluated. A primary shortcoming of both ChatGPT models was the tendency to generate irrelevant content lacking the needed focus. Although the overall ChatGPT performance in these diagnostic microbiology case scenarios might be described as "above average" at best, there remains a significant potential for improvement, considering the identified limitations and unsatisfactory results in a few cases.

Reply to Moreno et al. Comment on "Sallam, M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare 2023, 11, 887".

I would like to thank the authors for their commentary on the publication "ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns" [...].

Pervasive Parental Hesitancy and Resistance towards Measles Rubella Vaccination in Jordan.

Measles remains a highly contagious and potentially severe infectious disease, necessitating high vaccine coverage. However, misinformation and measles vaccine hesitancy/resistance have posed significant challenges to achieving this goal. The COVID-19 pandemic further exacerbated these challenges, leading to a measles outbreak in Jordan in 2023. This study aimed to investigate the acceptance of the measles rubella (MR) vaccine among parents in Jordan and to identify its associated determinants. This cross-sectional questionnaire-based study was conducted using a previously Arabic-validated version of the Parental Attitudes towards Childhood Vaccines (PACV) survey instrument. Data collection took place in October 2023, and the final study sample comprised a total of 391 parents, with mothers representing 69.8% of the participants (n = 273). The majority of participating parents expressed either resistance (n = 169, 43.2%) or hesitancy (n = 168, 43.0%) towards MR vaccination, while only 54 participants (13.8%) expressed MR vaccine acceptance. Multivariate analysis revealed that trust in vaccine safety/efficacy, behavior, and having fewer offspring were significantly associated with MR vaccine acceptance. The current study revealed a concerning level of MR vaccine hesitancy/resistance among parents in Jordan, which could signal a public health alarm in the country. Urgent and targeted interventions are strongly recommended to address this issue, including mass campaigns aimed at building trust in the MR vaccine's safety/efficacy. Additionally, there is an urgent need for effective public health initiatives to ensure sufficient measles vaccine coverage to prevent future outbreaks of this serious disease.

Vaccine conspiracy association with higher COVID-19 vaccination side effects and negative attitude towards booster COVID-19, influenza and monkeypox vaccines: A pilot study in Saudi Universities.

Conspiracies regarding vaccines are widely prevalent, with negative consequences on health-seeking behaviors. The current study aimed to investigate the possible association between the embrace of vaccine conspiracies and the attitude to booster COVID-19, seasonal influenza, and monkeypox (mpox) vaccinations as well as the perceived side effects following COVID-19 vaccination. The target population involved academic staff and university students in health colleges in the Kingdom of Saudi Arabia. A self-administered questionnaire was distributed in January 2023 to collect data on participants' demographics, self-reported side effects following each dose, willingness to get booster COVID-19, seasonal influenza, and mpox vaccinations, as well as an evaluation of vaccine conspiracies and attitude to mandatory vaccination. Among the 273 participants, the willingness to receive yearly booster COVID-19 vaccination was observed among 26.0% of the participants, while it was 46.9% and 34.1% for seasonal influenza and mpox vaccinations, respectively. Multinomial logistic regression analyses demonstrated a significant correlation between endorsing vaccine conspiracies and higher frequency of self-reported side effects following uptake of the second and third doses of COVID-19 vaccines. Vaccine conspiracies were also correlated with attitude toward booster COVID-19, influenza, mpox, and mandatory vaccination. The findings of this pilot study highlighted the potential adverse impact of the preexisting notions and negative attitudes toward vaccines, which could have contributed to heightened perceived side effects following COVID-19 vaccination. The study also highlighted the ongoing divisions concerning mandatory vaccination policies, emphasizing the need for cautious implementation of this strategy as a last resort for public health benefit.

Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models.

Background Artificial intelligence (AI)-based conversational models, such as Chat Generative Pre-trained Transformer (ChatGPT), Microsoft Bing, and Google Bard, have emerged as valuable sources of health information for lay individuals. However, the accuracy of the information provided by these AI models remains a significant concern. This pilot study aimed to test a new tool with key themes for inclusion as follows: Completeness of content, Lack of false information in the content, Evidence supporting the content, Appropriateness of the content, and Relevance, referred to as "CLEAR", designed to assess the quality of health information delivered by AI-based models. Methods Tool development involved a literature review on health information quality, followed by the initial establishment of the CLEAR tool, which comprised five items that aimed to assess the following: completeness, lack of false information, evidence support, appropriateness, and relevance. Each item was scored on a five-point Likert scale from excellent to poor. Content validity was checked by expert review. Pilot testing involved 32 healthcare professionals using the CLEAR tool to assess content on eight different health topics deliberately designed with varying qualities. The internal consistency was checked with Cronbach's alpha (α). Feedback from the pilot test resulted in language modifications to improve the clarity of the items. The final CLEAR tool was used to assess the quality of health information generated by four distinct AI models on five health topics. The AI models were ChatGPT 3.5, ChatGPT 4, Microsoft Bing, and Google Bard, and the content generated was scored by two independent raters with Cohen's kappa (κ) for inter-rater agreement. Results The final five CLEAR items were: (1) Is the content sufficient?; (2) Is the content accurate?; (3) Is the content evidence-based?; (4) Is the content clear, concise, and easy to understand?; and (5) Is the content free from irrelevant information? Pilot testing on the eight health topics revealed acceptable internal consistency with a Cronbach's α range of 0.669-0.981. The use of the final CLEAR tool yielded the following average scores: Microsoft Bing (mean=24.4±0.42), ChatGPT-4 (mean=23.6±0.96), Google Bard (mean=21.2±1.79), and ChatGPT-3.5 (mean=20.6±5.20). The inter-rater agreement revealed the following Cohen κ values: for ChatGPT-3.5 (κ=0.875, P<.001), ChatGPT-4 (κ=0.780, P<.001), Microsoft Bing (κ=0.348, P=.037), and Google Bard (κ=.749, P<.001). Conclusions The CLEAR tool is a brief yet helpful tool that can aid in standardizing testing of the quality of health information generated by AI-based models. Future studies are recommended to validate the utility of the CLEAR tool in the quality assessment of AI-generated health-related content using a larger sample across various complex health topics.