Data on comprehensive population-based surveillance of antimicrobial resistance is lacking. In low- and middle-income countries, the challenges are high due to weak laboratory capacity, poor health systems governance, lack of health information systems, and limited resources. Developing countries struggle with political and social dilemma, and bear a high health and economic burden of communicable diseases. Available data are fragmented and lack representativeness which limits their use to advice health policy makers and orientate the efficient allocation of funding and financial resources on programs to mitigate resistance. Low-quality data means soaring rates of antimicrobial resistance and the inability to track and map the spread of resistance, detect early outbreaks, and set national health policy to tackle resistance. Here, we review the barriers and limitations of conducting effective antimicrobial resistance surveillance, and we highlight multiple incremental approaches that may offer opportunities to strengthen population-based surveillance if tailored to the context of each country.

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.

Background: ChatGPT is a conversational large language model that has the potential to revolutionize knowledge acquisition. However, the impact of this technology on the quality of education is still unknown considering the risks and concerns surrounding ChatGPT use. Therefore, it is necessary to assess the usability and acceptability of this promising tool. As an innovative technology, the intention to use ChatGPT can be studied in the context of the technology acceptance model (TAM). Objective: This study aimed to develop and validate a TAM-based survey instrument called TAME-ChatGPT (Technology Acceptance Model Edited to Assess ChatGPT Adoption) that could be employed to examine the successful integration and use of ChatGPT in health care education. Methods: The survey tool was created based on the TAM framework. It comprised 13 items for participants who heard of ChatGPT but did not use it and 23 items for participants who used ChatGPT. Using a convenient sampling approach, the survey link was circulated electronically among university students between February and March 2023. Exploratory factor analysis (EFA) was used to assess the construct validity of the survey instrument. Results: The final sample comprised 458 respondents, the majority among them undergraduate students (n=442, 96.5%). Only 109 (23.8%) respondents had heard of ChatGPT prior to participation and only 55 (11.3%) self-reported ChatGPT use before the study. EFA analysis on the attitude and usage scales showed significant Bartlett tests of sphericity scores (P<.001) and adequate Kaiser-Meyer-Olkin measures (0.823 for the attitude scale and 0.702 for the usage scale), confirming the factorability of the correlation matrices. The EFA showed that 3 constructs explained a cumulative total of 69.3% variance in the attitude scale, and these subscales represented perceived risks, attitude to technology/social influence, and anxiety. For the ChatGPT usage scale, EFA showed that 4 constructs explained a cumulative total of 72% variance in the data and comprised the perceived usefulness, perceived risks, perceived ease of use, and behavior/cognitive factors. All the ChatGPT attitude and usage subscales showed good reliability with Cronbach α values >.78 for all the deduced subscales. Conclusions: The TAME-ChatGPT demonstrated good reliability, validity, and usefulness in assessing health care students’ attitudes toward ChatGPT. The findings highlighted the importance of considering risk perceptions, usefulness, ease of use, attitudes toward technology, and behavioral factors when adopting ChatGPT as a tool in health care education. This information can aid the stakeholders in creating strategies to support the optimal and ethical use of ChatGPT and to identify the potential challenges hindering its successful implementation. Future research is recommended to guide the effective adoption of ChatGPT in health care education.

Purpose: Since the term orthorexia nervosa (ON) was coined from the Greek (ὀρθός, right and ὄρεξις, appetite) in 1997 to describe an obsession with “correct” eating, it has been used worldwide without a consistent definition. Although multiple authors have proposed diagnostic criteria, and many theoretical papers have been published, no consensus definition of ON exists, empirical primary evidence is limited, and ON is not a standardized diagnosis. These gaps prevent research to identify risk and protective factors, pathophysiology, functional consequences, and evidence-based therapeutic treatments. The aims of the current study are to categorize the common observations and presentations of ON pathology among experts in the eating disorder field, propose tentative diagnostic criteria, and consider which DSM chapter and category would be most appropriate for ON should it be included. Methods: 47 eating disorder researchers and multidisciplinary treatment specialists from 14 different countries across four continents completed a three-phase modified Delphi process, with 75% agreement determined as the threshold for a statement to be included in the final consensus document. In phase I, participants were asked via online survey to agree or disagree with 67 statements about ON in four categories: A–Definition, Clinical Aspects, Duration; B–Consequences; C–Onset; D–Exclusion Criteria, and comment on their rationale. Responses were used to modify the statements which were then provided to the same participants for phase II, a second round of feedback, again in online survey form. Responses to phase II were used to modify and improve the statements for phase III, in which statements that met the predetermined 75% of agreement threshold were provided for review and commentary by all participants. Results: 27 statements met or exceeded the consensus threshold and were compiled into proposed diagnostic criteria for ON. Conclusions: This is the first time a standardized definition of ON has been developed from a worldwide, multidisciplinary cohort of experts. It represents a summary of observations, clinical expertise, and research findings from a wide base of knowledge. It may be used as a base for diagnosis, treatment protocols, and further research to answer the open questions that remain, particularly the functional consequences of ON and how it might be prevented or identified and intervened upon in its early stages. Although the participants encompass many countries and disciplines, further research will be needed to determine if these diagnostic criteria are applicable to the experience of ON in geographic areas not represented in the current expert panel. Level of evidence: Level V: opinions of expert committees

Antibiotic resistance is an ecosystem problem threatening the interrelated human-animal-environment health under the “One Health” framework. Resistant bacteria arising in one geographical area can spread via cross-reservoir transmission to other areas worldwide either by direct exposure or through the food chain and the environment. Drivers of antibiotic resistance are complex and multi-sectoral particularly in Lower- and Middle-income countries. These include inappropriate socio-ecological behaviors; poverty; overcrowding; lack of surveillance systems; food supply chain safety issues; highly contaminated waste effluents; and loose rules and regulations. In order to examine the drivers of antibiotic resistance from a “one health” perspective, a literature review was conducted on three databases including PubMed, Medline and Google Scholar. A total of 485 studies of potential relevance were selected, out of which 182 were included in this review. Results have shown that the aforementioned market failures are the leading cause for the negative externality of antibiotic resistance that extends in scope from the individual to the global ecosystem. Incremental and sustainable global actions can make the change, however, the problem will continue to prevail if governments do not prioritize the “One health” approach and if individual’s accountability is still denied in a world struggling with profound socio-economic problems.

Background: Psychiatry is a specialized field of medicine that focuses on the diagnosis, treatment, and prevention of mental health disorders. With advancements in technology and the rise of artificial intelligence (AI), there has been a growing interest in exploring the potential of AI language models systems, such as Chat Generative Pre-training Transformer (ChatGPT), to assist in the field of psychiatry.Objective: Our study aimed to evaluates the effectiveness, reliability and safeness of ChatGPT in assisting patients with mental health problems, and to assess its potential as a collaborative tool for mental health professionals through a simulated interaction with three distinct imaginary patients.Methods: Three imaginary patient scenarios (cases A, B, and C) were created, representing different mental health problems. All three patients present with, and seek to eliminate, the same chief complaint (i.e., difficulty falling asleep and waking up frequently during the night in the last 2°weeks). ChatGPT was engaged as a virtual psychiatric assistant to provide responses and treatment recommendations.Results: In case A, the recommendations were relatively appropriate (albeit non-specific), and could potentially be beneficial for both users and clinicians. However, as complexity of clinical cases increased (cases B and C), the information and recommendations generated by ChatGPT became inappropriate, even dangerous; and the limitations of the program became more glaring. The main strengths of ChatGPT lie in its ability to provide quick responses to user queries and to simulate empathy. One notable limitation is ChatGPT inability to interact with users to collect further information relevant to the diagnosis and management of a patient’s clinical condition. Another serious limitation is ChatGPT inability to use critical thinking and clinical judgment to drive patient’s management.Conclusion: As for July 2023, ChatGPT failed to give the simple medical advice given certain clinical scenarios. This supports that the quality of ChatGPT-generated content is still far from being a guide for users and professionals to provide accurate mental health information. It remains, therefore, premature to conclude on the usefulness and safety of ChatGPT in mental health practice.