Implications of technological advancement of surgery in low and middle-income countries' a narrative review.

Countries that are striving to keep pace with emerging technologies in surgical practices and still not able to cope with exemplary international standards are in dire need of resources to build and strengthen their healthcare system. This review focusses on the impeding factors that hinder in adaptation of advanced technology and machinery in the health care industry. Considering the immense potential for current surgical technologies to transform the delivery of healthcare, their implementation in LMICs confronts considerable challenges due to lack of infrastructure, human capital and inadequate resources. To address these difficulties, various entities, including healthcare institutions, government and non-governmental organisations, and foreign partners, must work together. Building capacity through intended education and training initiatives, building infrastructure, and collaborative partnerships are critical for overcoming hurdles to effective deployment of surgical technology in low-income communities of the world.

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work-a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students' perspectives regarding the use of such tools in school work, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use in school work. We find that ChatGPT's performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks.

COVID-19 lockdowns and children's health and well-being.

This paper studies the health and well-being of children during the COVID-19 lockdowns in a developing country context. Using surveys for low-income households in rural areas of Pakistan, we find that lockdowns are associated with worsened health and well-being of children. Exploring potential economic and noneconomic mechanisms behind this negative association, we find that children participating in the labor market due to extreme poverty suffer the worst impact from lockdowns. These results call for policies that target resources towards households where children's participation in the labor market is more likely since leaving vulnerable children behind will have a lasting economic impact for developing economies.

The impact of face masks on interpersonal trust in times of COVID-19.

Despite the widespread use of face masks to combat COVID-19, little is known about their social and behavioral consequences. To understand the impact of face masks on interpersonal trust, we designed a novel experiment to assess the causal impact of face mask use on whether individuals follow economically relevant advice from a stranger. From a survey of more than 2000 US citizens, conducted during July and August 2020, we find that almost 5% fewer individuals trust advice when it is given by someone wearing a mask than when it is given by someone not wearing a mask. While, surprisingly, health-related risks do not seem to alter the way masks affect trust, the effects of masks are particularly large among individuals whose households face economic risks due to COVID-19 and those with below-average normative beliefs about mask wearing. Our results highlight the non-health-related meaning that face masks have developed during COVID-19 and suggest that mask use undermines trust in others among a substantial share of the US population.