The delay or refusal of vaccination, which defines vaccine hesitancy, is a major challenge to successful control of COVID-19 epidemic. The huge number of publications addressing COVID-19 vaccine hesitancy necessitates periodic review to provide a concise summary of COVID-19 vaccine acceptance rates worldwide. In the current narrative review, data on COVID-19 vaccine acceptance rates were retrieved from surveys in 114 countries/territories. In East and Southern Africa (n = 9), the highest COVID-19 vaccine acceptance rate was reported in Ethiopia (92%), while the lowest rate was reported in Zimbabwe (50%). In West/Central Africa (n = 13), the highest rate was reported in Niger (93%), while the lowest rate was reported in Cameroon (15%). In Asia and the Pacific (n = 16), the highest rates were reported in Nepal and Vietnam (97%), while the lowest rate was reported in Hong Kong (42%). In Eastern Europe/Central Asia (n = 7), the highest rates were reported in Montenegro (69%) and Kazakhstan (64%), while the lowest rate was reported in Russia (30%). In Latin America and the Caribbean (n = 20), the highest rate was reported in Mexico (88%), while the lowest rate was reported in Haiti (43%). In the Middle East/North Africa (MENA, n = 22), the highest rate was reported in Tunisia (92%), while the lowest rate was reported in Iraq (13%). In Western/Central Europe and North America (n = 27), the highest rates were reported in Canada (91%) and Norway (89%), while the lowest rates were reported in Cyprus and Portugal (35%). COVID-19 vaccine acceptance rates ≥ 60% were seen in 72/114 countries/territories, compared to 42 countries/territories with rates between 13% and 59%. The phenomenon of COVID-19 vaccine hesitancy appeared more pronounced in the MENA, Europe and Central Asia, and Western/Central Africa. More studies are recommended in Africa, Eastern Europe and Central Asia to address intentions of the general public to get COVID-19 vaccination.

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.

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.

Vaccination to prevent influenza virus infection and to lessen its severity is recommended among healthcare workers (HCWs). Health professionals have a higher risk of exposure to viruses and could transmit the influenza virus to vulnerable patients who are prone to severe disease and mortality. The aim of the current study was to evaluate the levels of influenza vaccine acceptance and uptake as well as its determinants, among Jordanian HCWs over the last influenza season of 2021/2022. This study was based on a self-administered electronic survey that was distributed in March 2022. Psychological determinants of influenza vaccine acceptance and vaccine conspiracy beliefs were assessed using the previously validated 5C scale questionnaire (confidence, complacency, constraints, calculation and collective responsibility) and the vaccine conspiracy beliefs scale. The study sample comprised a total of 1218 HCWs: nurses (n = 412, 33.8%), physicians (n = 367, 30.1%), medical technicians (n = 182, 14.9%), pharmacists (n = 161, 13.2%) and dentists (n = 87, 7.1%), among others. About two-thirds of the study sample expressed willingness to receive influenza vaccination if provided free of charge (n = 807, 66.3%), whereas less than one-third were willing to pay for the vaccine (n = 388, 31.9%). The self-reported uptake of the influenza vaccine in the last influenza season was 62.8%. The following factors were significantly associated with higher acceptance of influenza vaccination if provided freely, as opposed to vaccine hesitancy/rejection: male sex; physicians and dentists among HCW categories; higher confidence and collective responsibility; and lower complacency, constraints and calculation. Higher influenza vaccine uptake was significantly correlated with nurses and physicians among HCW categories, older age, a higher monthly income, higher confidence and collective responsibility, lower complacency and constraints and lower embrace of general vaccine conspiracy beliefs. The results of the current study can provide helpful clues to improve influenza vaccine coverage among HCWs in Jordan. Consequently, this can help to protect vulnerable patient groups and reserve valuable resources in healthcare settings. Psychological determinants appeared to be the most significant factors for vaccine acceptance and uptake, whereas the embrace of general vaccine conspiracy beliefs was associated with lower rates of influenza vaccine uptake, which should be considered in educational and interventional measures aiming to promote influenza vaccination.

Pharmacy workforces are central to healthcare systems, yet the profession faces challenges in job satisfaction and retention due to evolving roles, workload pressures, and other issues. Understanding workforce stability is crucial for optimising pharmacy services. This systematic review aimed to identify and analyze the critical factors impacting pharmacy staff job satisfaction and retention, providing actionable insights to improve workforce stability and long-term engagement in the profession. A comprehensive search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), covering broad academic databases including EMBASE, Web of Science, PubMed, International Pharmaceutical Abstracts, and the supplementary use of Google Scholar for studies published between 2019 and 2024. The quality of the included articles was evaluated, revealing a generally low to moderate risk of bias. The review synthesised findings from 81 studies and extracted ten relevant themes. Countries including the United States, Saudi Arabia, Nigeria, Pakistan, and Southeast Asia countries contributed most frequently, highlighting regional research diversity. Key factors influencing job satisfaction included burnout, stress, and workload (24%); work conditions and roles (22%); professional development (14%); earnings and benefits (10%); and leadership support (9%). With a global perspective that travels across 36 countries in five continents, this study is the latest in-depth analysis of factors influencing job satisfaction in the pharmacy workforce. This review emphasises the need for policy reforms and further research on workplace conditions in different locations. It provides insights for policymakers and healthcare leaders to enhance the pharmacy workforce's strategic support and engagement initiatives.

The rise of superintelligent open-source generative AI (genAI) heralds both extraordinary potential and unprecedented risk, exemplified by the rapid emergence of DeepSeek as a global AI innovator. This perspective article examines the dual-edged nature of open source genAI technologies, highlighting their capacity to democratize innovation while exposing critical vulnerabilities. By providing affordable, high-performing, and openly available models like DeepSeek-R1 and DeepSeek-V3, this Chinese AI company has disrupted the proprietary dominance of Western AI giants. These advancements are expected to empower researchers in resource-limited settings, foster global collaboration, and enable breakthroughs across numerous fields. Open-source AI, as illustrated by DeepSeek, has the potential to redefine the technological landscape by making advanced capabilities accessible to underrepresented communities and encouraging ethical and inclusive innovation. However, the openness that drives such progress is fraught with existential risks. Superintelligent open-source models, accessible to anyone with minimal resources, lower barriers for misuse by malicious actors. From automated cyberattacks and disinformation campaigns to destabilizing critical infrastructures, the potential for harm is vast and unprecedented. Beyond immediate security concerns, these technologies threaten economic stability by displacing entire workforces and exacerbating inequalities, and they undermine human agency by enabling manipulation on an individual and societal level. This perspective seeks to explore the profound benefits of open-source superintelligent AI while critically addressing the urgent need for ethical and regulatory frameworks to mitigate its risks. The story of DeepSeek underscores the fragile balance between innovation and destruction in an era where technological progress outpaces safeguards. Humanity’s ability to harness the transformative power of open-source AI without succumbing to its destructive potential is not just a technological challenge—it is an existential imperative. This perspective argues for vigilance, responsibility, and global cooperation to ensure that the promise of open-source AI serves humanity rather than imperiling it.