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Record W4362733304 · doi:10.3390/jcm12082766

Depression, Anxiety, and Stress among Hangover-Sensitive and Hangover-Resistant Drinkers

2023· article· en· W4362733304 on OpenAlexaff
Andy J. Kim, Agnese Merlo, Marlou Mackus, Gillian Bruce, Sean Johnson, Chris Alford, Simon Sherry, Sherry H. Stewart, Joris C. Verster

Bibliographic record

VenueJournal of Clinical Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsDalhousie University
FundersUniversiteit Utrecht
KeywordsAnxietyMedicineDepression (economics)DemographicsAlcohol consumptionPsychiatryClinical psychologyStress (linguistics)AlcoholDemography

Abstract

fetched live from OpenAlex

This study investigated potential differences in baseline (i.e., non-hangover-related) levels of depression, anxiety, and stress between individuals who are sensitive to and those resistant to hangovers after consuming alcohol. Participants included 5111 university students from the Netherlands and the U.K., including 3205 hangover-sensitive and 1906 hangover-resistant drinkers. All participants completed surveys on their demographics, alcohol consumption, and hangover susceptibility (whether they experienced a hangover in the past 12 months), as well as their baseline levels of depression, anxiety, and stress on the DASS-21 scale. The results showed that hangover-sensitive drinkers had significantly higher levels of anxiety and stress, but not depression, compared to hangover-resistant drinkers. However, the observed differences between the two groups were small, with a magnitude of less than 1 out of 42 points on the DASS-21 anxiety and stress subscales, and are thus unlikely to be clinically meaningful.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.397
Teacher spread0.342 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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