MétaCan
Menu
Back to cohort
Record W4406088141 · doi:10.1080/07448481.2024.2440761

Young adult drinking during the COVID-19 pandemic: Examining the role of anxiety sensitivity, perceived stress, and drinking motives

2025· article· en· W4406088141 on OpenAlexaff
Charlotte Corran, Paul Norman, Roisin M. O’Connor

Bibliographic record

VenueJournal of American College Health · 2025
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsConcordia University
Fundersnot available
KeywordsAnxietyYoung adultAnxiety sensitivityPsychologyPandemicCoping (psychology)MediationClinical psychologyAlcoholCoronavirus disease 2019 (COVID-19)Human factors and ergonomicsInjury preventionSuicide preventionPoison controlEnvironmental healthDevelopmental psychologyPsychiatryMedicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Studies have shown that those high in anxiety were at increased risk for alcohol use during the COVID-19 pandemic. Tension reduction theory points to anxiety sensitivity (AS) as a potential risk factor. Drinking to cope may further increase this risk. During the pandemic, those high in AS may have experienced increased stress and drank to cope, which may have put them at risk for misusing alcohol. Objective: The current study tested the association between AS and alcohol outcomes, mediated by perceived stress and drinking motives, among young adults during the COVID-19 pandemic. Participants and Methods: Young adults (N = 143) self-reported on AS, perceived stress, drinking motives, and alcohol outcomes (i.e., use and problems). Results: A mediation analysis revealed that AS positively predicted alcohol problems, via coping motives, and positively predicted alcohol use, via perceived stress and enhancement/sociability motives. Conclusion: These results confirm AS-risk for young adult alcohol use during the pandemic and highlight perceived stress and drinking motives as mechanisms of risk.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.097
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.365
Teacher spread0.335 · 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 teacher head, 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of American College HealthSame topicCOVID-19 and Mental HealthFrench-language works237,207