Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Having ever used tobacco is a key surveillance metric. Existing tobacco use survey items differ in their inclusion of minimal use language, such as "even one or two puffs." This study aimed to quantify how minimal use language affects tobacco ever use prevalence estimated from adolescent surveys. Participants (N=5127) in the 2022 Teens, Nicotine, and Tobacco Project online panel survey of California adolescents (ages 12-17) were randomized to one of two differently worded ever use survey items (i.e., with or without minimal use language) for eight different tobacco products independently. For seven of the eight products (except hookah), minimal use language resulted in numerically higher ever use prevalence estimates. Averaged across all products, ever use prevalence was 0.7-percentage points higher when items included minimal use language (95% CI: 0.1, 1.4). Findings suggest that minimal use language yields modestly higher tobacco use prevalence, with implications for comparing and interpretating surveillance data.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906182PMC
http://dx.doi.org/10.1080/13645579.2024.2320144DOI Listing

Publication Analysis

Top Keywords

minimal language
24
survey items
8
language tobacco
8
tobacco prevalence
8
tobacco
7
minimal
6
language
6
impact survey
4
survey item
4
item wording
4

Similar Publications

Background: Recent studies suggest that large language models (LLMs) such as ChatGPT are useful tools for medical students or residents when preparing for examinations. These studies, especially those conducted with multiple-choice questions, emphasize that the level of knowledge and response consistency of the LLMs are generally acceptable; however, further optimization is needed in areas such as case discussion, interpretation, and language proficiency. Therefore, this study aimed to evaluate the performance of six distinct LLMs for Turkish and English neurosurgery multiple-choice questions and assess their accuracy and consistency in a specialized medical context.

View Article and Find Full Text PDF

Purpose: This study explores the potential of generative AI models to aid experts in developing scripts for pharmacokinetic (PK) models, with a focus on constructing a two-compartment population PK model using data from Hosseini et al.

Methods: Generative AI tools ChatGPT v3.5, Gemini v2.

View Article and Find Full Text PDF

Background: Hypertension is the most prevalent reversible risk for cardiovascular morbidity and mortality. Blood pressure (BP) control is poor nationally and varies by race/ethnicity, and there is minimal understanding of the impact of country of origin.

Objective: To examine racial/ethnic disparities in BP control among high-risk patients and among Latino patients disaggregated by country of origin.

View Article and Find Full Text PDF

In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data.

View Article and Find Full Text PDF

We estimated linear mixed-effects models to analyze changes in language patterns (as measured using Linguistic Inquiry and Word Count) among neurodiverse youth to introduce a novel assessment useful for research into the potential benefits of special interests while minimizing respondent and researcher burden.

View Article and Find Full Text PDF