Health Information Literacy and Competencies of Information Age Students: Results From the Interactive Online Research Readiness Self-Assessment (RRSA)

Purpose: The purpose of this paper is to examine work-to-family conflict and family-to-work conflict, taking into account generational cohort and life cycle stage differences. Design/methodology/approach: Survey participants (428 employed individuals with families) represented different generations and life cycles. Key variables were work/family characteristics and centrality, work-family and family-work conflict, and age. Findings: Generational differences in both directions were found. Gen X-ers reported the most work-family conflict, followed by Millennials and then Baby Boomers. Baby Boomers exhibited family-work conflict the most, followed by Gen X-ers, and then Millennials, a surprising finding given generational stereotypes. Some of these differences remained after controlling for children in the household (based on life cycle stage theory) and age. Millennials were highest in work centrality, whereas Baby Boomers were highest in family centrality. Employees with children ages 13-18 reported the most work-family conflict, and employees with children under the age of six reported the most family-work conflict. Research limitations/implications: This study found that generation and children in the household make a difference in work-family conflict, but it did not support some of the common generational stereotypes. Future studies should use a time-lag technique to study generational differences. To reduce work-family conflict, it is important to consider its directionality, which varies across generations and life cycle stages. Practical implications: This informs organizations on how to tailor interventions to help employees balance work/life demands. Originality/value: This study is the first to simultaneously examine both generation and life cycle stage (children in the household) in regard to work-family conflict.

Dirt Cheap and Without Prescription: How Susceptible are Young US Consumers to Purchasing Drugs From Rogue Internet Pharmacies?

The research identified the skills, if any, that health preprofessional students wished to develop after receiving feedback on skill gaps as well as any strategies they intended to use to address these gaps.A qualitative approach was used to elicit students' ...

Background: Sentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods. Objective: Popular sentiment analysis tools based on natural language processing (NLP; VADER [Valence Aware Dictionary for Sentiment Reasoning], TEXT2DATA [T2D], and Linguistic Inquiry and Word Count [LIWC-22]), and a large language model (ChatGPT 4.0) were compared with manually coded sentiment scores, as applied to the analysis of YouTube comments on videos discussing the opioid epidemic. Sentiment analysis methods were also examined regarding ease of programming, monetary cost, and other practical considerations. Methods: Evaluation methods included descriptive statistics, receiver operating characteristic (ROC) curve analysis, confusion matrices, Cohen κ, accuracy, specificity, precision, sensitivity (recall), F1-score harmonic mean, and the Matthews correlation coefficient. An inductive, iterative approach to content analysis of the data was used to obtain manual sentiment codes. Results: A subset of comments were analyzed by a second coder, producing good agreement between the 2 coders’ judgments (κ=0.734). YouTube social media about the opioid crisis had many more negative comments (4286/4871, 88%) than positive comments (79/662, 12%), making it possible to evaluate the performance of sentiment analysis models in an unbalanced dataset. The tone summary measure from LIWC-22 performed better than other tools for estimating the prevalence of negative versus positive sentiment. According to the ROC curve analysis, VADER was best at classifying manually coded negative comments. A comparison of Cohen κ values indicated that NLP tools (VADER, followed by LIWC’s tone and T2D) showed only fair agreement with manual coding. In contrast, ChatGPT 4.0 had poor agreement and failed to generate binary sentiment scores in 2 out of 3 attempts. Variations in accuracy, specificity, precision, sensitivity, F1-score, and MCC did not reveal a single superior model. F1-score harmonic means were 0.34-0.38 (SD 0.02) for NLP tools and very low (0.13) for ChatGPT 4.0. None of the MCCs reached a strong correlation level. Conclusions: Researchers studying negative emotions, public worries, or dissatisfaction with social media face unique challenges in selecting models suitable for unbalanced datasets. We recommend VADER, the only cost-free tool we evaluated, due to its excellent discrimination, which can be further improved when the comments are at least 100 characters long. If estimating the prevalence of negative comments in an unbalanced dataset is important, we recommend the tone summary measure from LIWC-22. Researchers using T2D must know that it may only score some data and, compared with other methods, be more time-consuming and cost-prohibitive. A general-purpose large language model, ChatGPT 4.0, has yet to surpass the performance of NLP models, at least for unbalanced datasets with highly prevalent (7:1) negative comments.