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State and Federal Legislators’ Responses on Social Media to the Mental Health and Burnout of Health Care Workers Throughout the COVID-19 Pandemic: Natural Language Processing and Sentiment Analysis

State and Federal Legislators’ Responses on Social Media to the Mental Health and Burnout of Health Care Workers Throughout the COVID-19 Pandemic: Natural Language Processing and Sentiment Analysis

Significant correlations with a Benjamini-Hochberg–corrected P value of We applied the Valence Aware Dictionary and Sentiment Reasoner (VADER) [47], a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, to the baseline set of language features to identify weekly changes in post sentiment over time across political parties.

Matthew P Abrams, Arthur P Pelullo, Zachary F Meisel, Raina M Merchant, Jonathan Purtle, Anish K Agarwal

JMIR Infodemiology 2023;3:e38676

Association Between Online Reviews of Substance Use Disorder Treatment Facilities and Drug-Induced Mortality Rates: Cross-Sectional Analysis

Association Between Online Reviews of Substance Use Disorder Treatment Facilities and Drug-Induced Mortality Rates: Cross-Sectional Analysis

The Benjamini-Hochberg P correction and P Because the multilevel mixed-effects linear regression model accounts for variation at the facility level, all states with facilities with at least 5 online ratings were included (n=51 states). We used null random-intercepts models to calculate intraclass correlations and variance partitioning coefficients to determine the degree of clustering in ratings at the facility and state levels.

Matthew P Abrams, Raina M Merchant, Zachary F Meisel, Arthur P Pelullo, Sharath Chandra Guntuku, Anish K Agarwal

JMIR AI 2023;2:e46317

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