Search

Extensive economic research demonstrates correlations between unions with wages, income inequality, health insurance, discrimination, and other factors. Corresponding epidemiologic literature demonstrates correlations between income, income inequality, insurance, discrimination, and other factors with health. The first purpose of this narrative review is to link these literatures and identify 28 possible pathways whereby labor unions might affect the health of workers. This review is restricted to effects within workplaces; we do not consider unions' political activities. This review covers studies from the US, Europe, and Canada from 1980 through April 1, 2021. Pathways are grouped within five domains informed by the CDC 5-domain model of social determinants of health and the traditional 3-domain model of occupational medicine. Linked pathways include wages, inequality, excessive overtime, job satisfaction, employer-provided health insurance (EPHI), and discrimination. Second, we identify studies analyzing correlations between unions directly with health outcomes that do not require links. Outcomes include occupational injuries, sickness absence, and drug overdose deaths. Third, we offer judgments on the strength of pathways and outcomes --- labeled "consensus," "likely," "disputed" or "unknown" --- based on literature summaries. In our view, whereas there are four "consensus" pathways and outcomes and 16 "likely" pathways and outcomes for unions improving health, there are no "consensus" or "likely" pathways for harming health. The strongest "consensus" pathways and outcomes with salubrious associations include EPHI, OSHA inspections, dangerous working conditions, and injury deaths. Fourth, we identify research gaps and suggest methods for future studies. Unions are an underappreciated social determinant of health., (© 2021 The Author(s).)

Background: The #MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began., Objective: The aim of this study is to document, characterize, and quantify early public discourse and conversation of the #MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events., Methods: We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase "MeToo" from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse., Results: We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. We then estimated that in the first week of the movement, 11% of novel English language tweets with the words "MeToo" revealed details about the poster's experience of sexual assault or abuse and 5.8% revealed early life experiences of such events. We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement., Conclusions: These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement., (©Sepideh Modrek, Bozhidar Chakalov. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.09.2019.)