Clinical Relevance: Children with uncorrected visual impairment have lower scores on a variety of motor and cognitive tests. Exploring the influencing factors of low-income groups seeking vision care services is helpful for identifying relevant barriers and necessary measures to improve the utilization rate of vision care services.
Background: The community-based vision center (VC) is a popular model for solving vision problem of students in rural China. Compliance is the key factor to the success of the VC model. Factors determining compliance with visitations to VC among primary school students after screening were explored.
Methods: A cross-sectional study was conducted with 15,763 students from 228 primary schools. Information was collected through questionnaires and vision examinations. The determinants that affect visits of students to the VC were analyzed using logistic regression.
Results: Among the 15,763 sample, 5,361 (34%) students had a visual impairment. At baseline, only 962 (18%) of students with visual impairment sought vision care services. After a local VC was established, among the 5,163 students who needed to be referred, only 2,237 (43.33%) students visited the VC. Multivariate logistic regression models for predicting students visit the VC revealed that the following characteristics were significant predictors: poor uncorrected visual acuity (P < 0.001), a higher grade level (P = 0.008; P = 0.010), 'left-behind' children (P < 0.001), short living distance between home and the VC (P < 0.001), and the fact that these students lived in Gansu province (P < 0.001).
Conclusions: Establishing a VC that provides students with vision screening and free vision care services can increase the rate of seeking vision health services for students in rural areas, but the compliance rate still needs to be improved. The influencing factors for student compliance have been identified.
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http://dx.doi.org/10.1080/08164622.2021.1988521 | DOI Listing |
Graefes Arch Clin Exp Ophthalmol
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15-20 National Ophthalmologic Center, Paris, France.
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Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.
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Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology&Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
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