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Record W67129980 · doi:10.3233/wor-2003-00316

Occupational variations in drinking and psychological distress: A multilevel analysis

2003· article· en· W67129980 on OpenAlexaff
Alain Marchand, Andrée Demers, Pierre Durand, Marcel Simard

Bibliographic record

VenueWork · 2003
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMultilevel modelSocioeconomic statusPsychological distressPsychologyMultivariate analysisDistressPopulationMultivariate statisticsClinical psychologyEnvironmental healthMedicineMental healthPsychiatry

Abstract

fetched live from OpenAlex

The relationship between alcohol intake and psychological distress has been overlooked in studies on the working population. Using a multilevel multivariate model, this study reports results obtained from a sample of 8812 workers nested in 387 occupations. Results show that alcohol intake and psychological distress vary significantly at the worker and occupation levels, but they do not show a large variation at the occupation level. Occupational socioeconomic status appears to be a common factor explaining the correlation between alcohol intake and psychological distress at the occupation level. Semi-professionals, middle management, foreman and semiskilled clerical-sales-services occupations are particularly at risk. Gender is related to both outcomes, while work schedule and number of weekly working hours are associated only with psychological distress. Implications and limitations of these results are discussed.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.430
Teacher spread0.367 · 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

Citations29
Published2003
Admission routes1
Has abstractyes

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