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Record W4307035481 · doi:10.2196/38163

Substance Use From Social Distancing and Isolation by US Nativity During the Time of COVID-19: Cross-sectional Study

2022· article· en· W4307035481 on OpenAlexvenueno aff
Francisco Alejandro Montiel Ishino, Kevin Villalobos, Faustine Williams

Bibliographic record

VenueJMIR Public Health and Surveillance · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNHLBI Division of Intramural ResearchNational Institutes of Health
KeywordsPandemicMental healthSocial distancePsychosocialEnvironmental healthCoping (psychology)MedicineSocial isolationCross-sectional studySubstance abusePsychologyCoronavirus disease 2019 (COVID-19)PsychiatryInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic had many unprecedented secondary outcomes resulting in various mental health issues leading to substance use as a coping behavior. The extent of changes in substance use in a US sample by nativity has not been previously described. OBJECTIVE: This study aimed to design a web-based survey to assess the social distancing and isolation issues exacerbated by the COVID-19 pandemic to describe substance use as a coping behavior by comparing substance use changes before and during the pandemic. METHODS: A comprehensive 116-item survey was designed to understand the impact of COVID-19 and social distancing on physical and psychosocial mental health and chronic diseases. Approximately 10,000 web-based surveys were distributed by Qualtrics LLC between May 13, 2021, and January 09, 2022, across the United States (ie, continental United States, Hawaii, Alaska, and territories) to adults aged ≥18 years. We oversampled low-income and rural adults among non-Hispanic White, non-Hispanic Black, Hispanic or Latino, and foreign-born participants. Of the 5938 surveys returned, 5413 (91.16%) surveys were used after proprietary expert review fraud detection (Qualtrics) and detailed assessments of the completion rate and the timing to complete the survey. Participant demographics, substance use coping behaviors, and substance use before and during the pandemic are described by the overall US resident sample, followed by US-born and foreign-born self-reports. Substance use included the use of tobacco, e-cigarettes or nicotine vapes, alcohol, marijuana, and other illicit substances. Marginal homogeneity based on the Stuart-Maxwell test was used to assess changes in self-reported substance use before and during the pandemic. RESULTS: The sample mostly included White (2182/5413, 40.31%) and women participants (3369/5406, 62.32%) who identified as straight or heterosexual (4805/5406, 88.88%), reported making ≥US $75,000 (1405/5355, 26.23%), and had vocational or technical training (1746/5404, 32.31%). Similarities were observed between the US-born and the foreign-born participants on increased alcohol consumption: from no alcohol consumption before the pandemic to consuming alcohol once to several times a month and from once to several times per week to every day to several times per day. Although significant changes were observed from no prior alcohol use to some level of increased use, the opposite was also observed and was more pronounced among foreign-born participants. That is, there was a 5.1% overall change in some level of alcohol use before the pandemic to no alcohol use during the pandemic among foreign-born individuals, compared with a 4.3% change among US-born individuals. CONCLUSIONS: To better prepare for the inadvertent effects of public health policies meant to protect individuals, we must understand the mental health burdens that can precipitate into substance use coping mechanisms that not only have a deleterious effect on physical and mental health but also exacerbate morbidity and mortality in a disease like COVID-19.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.401
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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