Introduction: Alcohol, cannabis, and nicotine are commonly used psychoactive substances that affect adolescent neurocognition. Little is known about the educational impacts of their use on measures of educational performance, participation and problems, especially among youth with a chronic illness who may use these substances to alleviate stress and symptoms.
Methods: Adolescents receiving general or subspecialty care were administered an electronic survey from 2016 to 2018. Data were analyzed in 2023. Using modified Poisson models, cross-sectional associations between past 12-month usage of alcohol, cannabis, and/or nicotine and educational impacts were estimated.
Results: Among 958 adolescents (mean age 16.0 years (SD 1.3), 564 (58.9%) female gender, 445 (46.5%) in subspecialty care), 294 (30.7%), 220 (23.0%), and 126 (13.2%) reported past 12-month use of alcohol, cannabis, and nicotine respectively, while 407 (42.5%) reported ≥1 educational impact, including recent lower grades 210 (21.9%), past 3-month truancy from school 164 (17.1%) or activities 170 (17.7%), and detention 82 (8.6%). Use of cannabis, but not other substances, was associated with negative educational impacts: lower grades (mostly C's/D's/F's), adjusted prevalence ratios [APR, (95% CI)] 1.54 (1.13-2.11); past 3-month truancy from school [2.16 (1.52-3.07)]; detention [2.29 (1.33-3.94)]. The association between cannabis use and any negative educational impact was stronger among adolescents with a chronic illness (p<0.001).
Conclusions: Among adolescents, cannabis use was associated with a heightened risk of negative educational impacts, even after controlling for alcohol and nicotine use. Adolescents with chronic illness were especially likely to experience negative educational impacts. Findings underscore need for preventive interventions and messaging to reduce risks.
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http://dx.doi.org/10.1016/j.amepre.2023.09.029 | DOI Listing |
Sci Rep
December 2024
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, 430070, China.
Urban rail transit systems, represented by subways, have significantly alleviated the traffic pressure brought by urbanization and have addressed issues such as traffic congestion. However, as a commonly used construction method for subway tunnels, shield tunneling inevitably disturbs the surrounding soil, leading to uneven ground surface settlement, which can impact the safety of nearby buildings. Therefore, it is crucial to promptly obtain and predict the ground surface settlement induced by shield tunneling construction to enable safety warnings and evaluations.
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December 2024
Department of Dermatology, Niazi Hospital, Lahore, Pakistan.
With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.
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December 2024
Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Because a significant portion of oil remains in carbonate reservoirs, efficient techniques are essential to increase oil recovery from carbonate reservoirs. Wettability alteration is crucial for enhanced oil recovery (EOR) from oil-wet reservoirs. This study investigates the impact of different substances on the wettability of dolomite and calcite rocks.
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December 2024
Department of Chemistry, Illinois State University, Normal, IL, 61790-4160, USA.
This work aims to address key issues in the ballistic performance of ceramic-based composite armor, particularly at the joints of spliced bulletproof panels. The edge structure of C/C-SiC ceramic plates and ultra-high molecular weight polyethylene is redesigned to superimpose the joint areas. These structurally optimized composite pads are examined by numerical simulation of impact dynamics to understand their anti-penetration performance whose accuracy is then validated by live fire tests.
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December 2024
Department of Psychology, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, 475000, Malaysia.
This study evaluates the ability of large language models (LLMs) to deliver criterion-based grading and examines the impact of prompt engineering with detailed criteria on grading. Using well-established human benchmarks and quantitative analyses, we found that even free LLMs achieve criterion-based grading with a detailed understanding of the criteria, underscoring the importance of domain-specific understanding over model complexity. These findings highlight the potential of LLMs to deliver scalable educational feedback.
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