A5005492727- Youth Development and Social Support
- Smoking Behavior and Cessation
- Early Childhood Education and Development
- Behavioral and Psychological Studies
- Healthcare Policy and Management
- Health Systems, Economic Evaluations, Quality of Life
- Health, Medicine and Society
- Obesity, Physical Activity, Diet
- Family and Disability Support Research
- Diverse Educational Innovations Studies
- Behavioral Health and Interventions
- Child Development and Digital Technology
- Cannabis and Cannabinoid Research
- Adolescent and Pediatric Healthcare
- Educational and Psychological Assessments
- Impact of Technology on Adolescents
- Misinformation and Its Impacts
- Health Policy Implementation Science
- Workplace Health and Well-being
- Alcoholism and Thiamine Deficiency
- Public Health Policies and Education
- Nutrition, Health and Food Behavior
- Media Influence and Health
- Sentiment Analysis and Opinion Mining
- Adventure Sports and Sensation Seeking
Yale University
2023-2025
Office of Minority Health
2022
Florida International University
2017-2021
California University of Pennsylvania
2015
Philadelphia University
2014
University of Pennsylvania
2014
St. Thomas University
2011
University of Ottawa
2003
Abstract Introduction Previous research has identified abundant e-cigarette content on social media using primarily text-based approaches. However, frequently used platforms among youth, such as TikTok, contain visual content, requiring the ability to detect e-cigarette-related across large sets of videos and images. This study aims use a computer vision technique objects in TikTok videos. Aims Methods We searched 13 hashtags related vaping (eg, #vape) November 2022 obtained 826 still images...
Background Social media research is confronted by the expansive and constantly evolving nature of social data. Hashtags keywords are frequently used to identify content related a specific topic, but these search strategies often result in large numbers irrelevant results. Therefore, methods needed quickly screen based on question. The primary objective this article present generative artificial intelligence (AI; e.g ., ChatGPT) machine learning from platforms. As proof concept, we apply...
OBJECTIVES We assessed awareness and perceptions of, information sources about, engagement in modifying electronic nicotine delivery systems (ENDS) among adolescents young adults (AYAs). METHODS AYAs (N = 1018) endorsing past-month ENDS use completed a survey on of the following modifications: (1) refilling rechargeable cartridges/pods or (2) disposable pods, (3) rewicking (4) recharging (5) e-liquids (eg, changing propylene glycol/vegetable glycerin, nicotine), (6) combining cannabis for...
The study examines comprehension after oral and silent reading in elementary- middle-school students. It investigates whether when one mode is superior to the other for as children develop, independent of ability levels. One hundred seventy three first through seventh grades orally silently read grade-appropriate passages answered questions. A clear grade-related trend was found which fifth grades. In sixth grade, neither comprehension. Finally, emerged better grade. Vygotskian model...
In the United States, health insurance coverage for autism spectrum disorder treatments has been historically limited. response, as of 2015, 40 states and Washington, DC, have passed state mandates requiring many plans in private market to cover diagnostic treatment services. This study examined five states’ experiences implementing mandates. Semi-structured, key-informant interviews were conducted with 17 participants representing consumer advocacy organizations, provider companies....
Background With high rates of both e-cigarette and social media use among adolescents young adults (AYAs), influencers who promote e-cigarettes are particularly concerning but understudied. We examined the association between AYAs' 11 different platforms (e.g. Facebook, Twitter, Instagram, TikTok, YouTube) exposure to influencers.
The use of hashtags is a common way to promote e-cigarette content on social media. Analysis may provide insight into promotion However, the examination text data complicated by voluminous amount media data. This study used machine learning approaches (i.e., Bidirectional Encoder Representations from Transformers [BERT] topic modeling) identify TikTok.
Highlights Resource and workforce challenges impede adoption of evidence‐based practice in after‐school programs. Academic–community partnerships inform recommendations that align with individual program goals. We describe a three‐tiered approach to support online, workshop, on‐site components. Content prioritizes mental health kernels: emotion regulation, communication, problem‐solving. Support leverages teachable moments inherent recreation harnesses staff talent expertise.
E-cigarettes are frequently promoted on social media and portrayed in ways that attractive to youth. While the COVID-19 pandemic significantly affected people's lives, less is known about how influenced e-cigarette-related marketing information media. This study examined e-cigarettes were youtube, one of most popular platforms during pandemic.
Electronic nicotine delivery systems (ENDS) were created to vape e-liquids; however, social media demonstrates increased ENDS modifications cannabis. Analysis of content helps with understanding for cannabis use, overlapping markets and cannabis, the need additional regulation.
This study sought to validate a new measure, the Classroom Cohesion Survey (CCS), designed examine relationship between teachers and classroom assistants in autism support classrooms. Teachers, assistants, external observers showed good inter-rater agreement on CCS internal consistency for all scales. Simple factor structures were found both teacher- assistant-rated scales, with one-factor solutions Paired t tests revealed that average, rated cohesion stronger than teachers. The may be an...
Abstract Ongoing pressure for public schools to prioritize academics has increased attention on after‐school settings as a critical space social‐emotional learning (SEL). After‐school programs are uniquely positioned build protective and promotive factors that contribute positive future orientation, especially within communities where systemic inequities create barriers high school graduation, higher education, employment, earnings. This study examines Fit2Lead Youth Enrichment Sports (YES),...
Background Previous studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based data, analysis image-clustering has rarely Image clustering can identify underlying patterns structures across large sets of images, enabling more streamlined distillation visual data This study to examine e-cigarette-related images