OUTLIERS SÃO OUTSIDERS? PERFIL DOS DEPUTADOS ESTADUAIS “HEAVY USERS” DE MÍDIAS DIGITAIS NA LEGISLATURA 2019-2023

O objetivo deste artigo é fazer um estudo do uso das midias digitais pelos deputados estaduais brasileiros na legislatura 2019-2023. Procuraremos responder à seguinte questão: os parlamentares intensivos no uso das midias digitais em cada unidade da federação brasileira (outliers) podem também ser considerados “outsiders”, ou seja, parlamentares novatos na atividade política? Para verificar este fenômeno analisaremos o uso das duas midias digitais públicas mais utilizadas pelos deputados estaduais brasileiros no período 2019-2020, ou seja, Facebook e Instagram, verificando se este uso está associado a certas características da carreira política dos deputados. Os resultados indicam que esse fenômeno ocorre no Instagram, mas não no Facebook, ou seja, mídias sociais públicas de uso mais recente são usadas com mais intensidade por parlamentares “outsiders”.

Central stress allostasis among deprived and continuing marijuana users and non-users

Objective: We examined central nervous system stress allostasis (i.e., stress responses) among deprived and continuing heavy marijuana users and non-users. Method: Participants (N=210; 46.7% female; mean age=21.99; 91.4% White, 94.3% Non-Hispanic) were heavy marijuana users (N=134) and non-users (N=76). Heavy users were randomly assigned to a 3-day marijuana deprivation condition (N=68) or to continue using regularly (N=66). Participants completed 2 threat-of-shock stressor tasks that manipulated stressor predictability by varying shock probability or timing. We measured central stress allostasis via startle potentiation (stressor conditions minus matched no-stressor condition). We examined two group contrasts (heavy use: all heavy users vs. non-users; deprivation: deprived vs. continuing heavy users) on startle potentiation overall and moderated by stressor predictability (unpredictable vs. predictable). Results: Deprivation did not affect startle potentiation overall (timing task: p=0.184; probability task: p=0.328) or by stressor predictability (timing task: p=0.340; probability task: p=0.488). Heavy use did not affect startle potentiation overall (timing task: p=0.213; probability task: p=0.843) or by stressor predictability (timing task: p=0.683; probability task: p=0.348). Post-hoc analyses showed a general startle reactivity X deprivation interaction on startle potentiation overall (timing task: p=0.019; probability task: p=0.056) and by stressor predictability in the probability task (p=0.022) but not in the timing task (p=0.374). Conclusions: A history of marijuana use or acute deprivation did not alter central stress allostasis despite prominent theoretical expectations. This study adds to growing research on central stress allostasis in individuals with a history of drug use and begins to parse moderating roles of individual differences and stressor characteristics.

Segmenting clinicians’ usage patterns of a digital health tool: methodology and initial results (Preprint)

BACKGROUND Digital health tools allow clinicians to keep up with the expanding evidence base and to provide safer and more accurate care. However, even when cost is removed as a barrier, digital health tools require active, iterative implementation based on a nuanced understanding of the diverse needs of the health care workforce. OBJECTIVE To maximize the impact of a digital health tool, we aimed to understand clinicians’ usage patterns. Here, we present our method for segmenting clinicians based on their online behaviors. METHODS We collected 12 months of clickstream data (a record of users’ clicks within the tool) as well as repeated surveys. Our program collaborates with a digital tool, UpToDate, to facilitate free subscriptions to clinicians serving vulnerable populations globally. We enrolled 1,681 clinicians from 75 countries who applied to our program over a 9-week period. We calculated the total number of sessions, time spent online, type of activity (navigating, reading, or account management), calendar period of use, percent of days active on the site, and minutes of use per active day. We defined behavioral segments based on the distributions of these statistics. RESULTS A little more than half of clinicians (54%) used UpToDate for essentially the full year. On days when users logged on, they spent a median of 4.4 minutes on the site and an average of 71% of their time reading medical content. Based on period of use and minutes online, we defined five behavioral segments of users: short-term, light users (25%); short-term, heavy users (15%); long-term, heavy users (24%); long-term, light users (22%); and never-users (15%). CONCLUSIONS We believe these new behavioral segments can help inform the implementation of digital health tools, identify users who may need assistance, tailor training and messaging for users, and support research on digital health efforts. Methods combining clickstream with demographic and survey data have the potential to inform global health implementation. Our forthcoming analysis will use these methods to better elucidate what drives use.

Changes in Late Adolescent Marijuana Use During the COVID-19 Outbreak Vary as a Function of Typical Use

Adolescent marijuana use is a significant public health concern given that many individuals first begin using during this developmental period and an earlier age of onset is prospectively associated with numerous marijuana misuse outcomes. The outbreak of COVID-19 has resulted in stay-at-home orders and social distancing guidelines across the United States. For many adolescents, these orders resulted in a number of changes that could alter one’s marijuana use including changes to marijuana availability, parental supervision, amount of free time, and stress levels. Despite these possible changes, the impacts of the COVID-19 outbreak on adolescent marijuana use are unknown. The aims of this analysis were to 1) assess changes to marijuana use among late adolescents related to the COVID-19 outbreak, and 2) examine whether these changes vary as a function of one’s pre-COVID-19 levels of use. Data described here come from a screening survey for a larger study which was completed by 156 adolescents (ages 14-18, 78% male) after the stay-at-home order was put in place in Washington state on March 23rd, 2020. All participants completed a self-report questionnaire that included demographic information, marijuana use, and changes to marijuana use following the state’s stay-at-home order. In the sample, 55 participants described themselves as never having tried marijuana, and none of these participants reported having used during the COVID-19 outbreak. Of the 101 participants who reported any prior marijuana use, 44 reported stopping or decreasing their use as a result of COVID-19, 30 reported using similar amounts as before, and 27 reported increased marijuana use as a result of COVID-19. A chi-square test of independence revealed that changes in use significantly varied as a function of pre-COVID-19 levels of use, X2 (2, N = 98) = 29.79, p < .001. The odds of irregular and light marijuana users decreasing their use was 13.73 times higher than moderate and heavy users. Moderate and heavy users had higher odds of maintaining their current use (5.04 times higher) and increasing their use (3.07 times higher) compared to irregular and light users during the COVID-19 outbreak. Primary reasons given for decreasing use included decreased availability and less socialization. Primary reasons for increasing use included more free time, fewer responsibilities, and coping with stress and anxiety. The findings suggest that although marijuana use may appear to decrease on average across a range of late adolescents that vary according to their regular use, these decreases are not likely among moderate and heavy users who may actually be at increased risk of marijuana misuse during the COVID-19 outbreak.

Who Are the Heavy Users of Acute and Long-Term Care Services in Changhua County, Taiwan?

Identification of heavy health care users among community-dwelling older adults can lead to strategic care planning and management that positively impacts health care use and costs. This study aims to identify risk factors associated with heavy users of acute care and long-term care (LTC) in Changhua County, Taiwan. Study participants (n=8,090) included residents (age 65+) of Changhua County. Data were collected from 4/1/2017-10/26/2018. Linked hospitalization and LTC use information was provided by the Changhua County Public Health Bureau. Hospitalization was grouped into 1 vs. 2+. Univariate and multivariate logistic models will be used to address the study aim. Preliminary results show the average age was 81 years ranging from 65-105. Of this population, 56% are female, and 10% live alone. The average length of hospital stay was 9.7 days. Risk factors associated with heavy health care users identified in multivariate analysis will be presented and intervention strategies will be discussed. Part of a symposium sponsored by International Comparisons of Healthy Aging Interest Group.

Equities and Exchanges, 1993–2006

By the 1990s the pressures on traditional stock exchanges were so intense that inertia was no longer an option. These pressures included the globalization of investment, deregulation, dismantling of capital controls, cheap and rapid communication, and powerful computing, The effect was to undermine the grip that exchanges had once exerted over national stock markets. No longer were the members of exchanges the filter through which buying and selling passed because of the control they exercised over access to both information and the market. Alternative means of trading stocks were proliferating, undermining and then destroying the exclusive privileges long enjoyed by those belonging to stock exchanges. Leading this attack on the power of stock exchanges were the megabanks. As these banks grew in scale and scope, extending their activities around the globe, they were either able to internalize many transactions or trade between themselves. In the process they cut out the exchanges, bypassing, and the charges and restrictions they imposed. There had long been an ambiguous relationship between banks and exchanges, as they were both rivals and heavy users. The combination of the megabanks, interdealer brokers, and electronic markets was rendering exchanges redundant in the 1990s, forcing them to respond through diversification and mergers.