Differences in Practitioner Experience, Practice Type, and Profession in Attitudes Toward Growing Contact Lens Practice

OBJECTIVE: To investigate eye care practitioners' attitudes and perceptions toward potential interventions that can enhance contact lens (CL) practice across the world, and how this is influenced by their practice setting. METHODS: A self-administered, anonymized survey was constructed in English and then forward and backward translated into six more languages. The survey was distributed online via social media platforms and mailing lists involving reputed international professional bodies. RESULTS: In total, 2,222 responses from 27 countries with sufficient responses were analyzed (53% females, median age- 37 years). Most of the respondents were optometrists (81.9%) and 47.6% were from stand-alone/independent practices. Median working experience in CL prescribing was 11.0 years (IQR: 18.0, 4-22 years). Over two-third of them declared themselves to be very hopeful (22.9%) or hopeful (45.1%) about the future of their CL practice. Among the potential interventions proposed, continuous update of knowledge and skills and competently managing CL-related complications were rated the most important (median score: 9/10 for each). Practitioners working in national/regional retail chains expressed higher proactivity in recommending CLs (9/10) than those in local chains, hospitals, and universities (for all 8/10, P <0.05). National differences were also identified in eye care practitioner attitudes and perceptions ( P <0.05). CONCLUSIONS: The study provided important information to delineate a variety of elements characterizing CL practice across the world. These insights can serve as a basis to design strategies at national and international levels

Opportunities and threats to contact lens practice:A global survey perspective

Aim: To understand the views of contact lens (CL) practitioners across the globe regarding what they perceive as opportunities and threats in CL practice. Methods: A self-administered anonymised questionnaire, constructed in English and translated in six more languages, was distributed through reputed international professional bodies and academic institutions worldwide. The questionnaire included items on demographic characteristics, type of practice, and questions designed to explore practitioners’ perspective on the future of their CL practice over the next five years. Results: A total of 2408 valid responses were analysed. Multifocal CLs for presbyopia, CLs for myopia control, use of daily disposable (DD) CLs for occasional wear, and biocompatible materials to improve comfort were identified as promising areas of opportunities by practitioners (all 8/10). Respondents from North America, and Europe valued DDCLs for occasional wear moderately more favourable (Median: 9/10 for all) as compared to colleagues in Asia (Median: 8/10, p < 0.001), South America (Median: 8/10, p < 0.01), and Africa (Median: 8/10p < 0.01). Multifocal CLs for presbyopia was perceived as a better opportunity by practitioners in North America and Europe (Median: 9/10 for both), as well as in Australasia (Median: 8/10), in comparison to Asia, Africa, and Middle East (for all Median: 6/10, p < 0.001). Practitioners expressed concerns about the availability of CLs and CL prescriptions online without direct professional involvement (both 9/10). Conclusions: Overall, the most appealing opportunities for CL practice growth were identified in occasional use of DD CLs, biocompatible materials to reduce CL discomfort, multifocal CLs for presbyopia correction and management of myopia control with CLs. Lack of regulation in CL sales, especially online, seemed to be a constant threat. The insights from this study can be used to design targeted strategies to enhance CL practice across the globe and in specific geographical areas

Proceedings of the 2nd International Conference on Modern Trends in Engineering Technology and Management

This proceeding contains articles on the various ideas of the academic community presented at The 2nd International Conference on Modern Trends in Engineering Technology and Management (ICMEM 2023) organized by the Sree Narayana Institute of Technology Adoor-691554, Kerala, India on 4th-6th May 2023.  ICMEM 2023 aimed to provide a forum for the exchange of ideas, issues, challenges, discoveries, opportunities, and applications of Modern Trends in Engineering Technology and Management. The ever-changing scope and rapid development of science and technology generate new problems, questions, and curiosity, necessitating the exchange of brilliant ideas and raising awareness of this vital research field in a variety of directions.  Conference Title: 2nd International Conference on Modern Trends in Engineering Technology and ManagementConference Acronyms: ICMEM 2023Conference Date: 4th-6th May 2023Conference Location: Hybrid ModeConference Organizer: SNIT Adoor, Kerala, Indi

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc