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Record W1892869757 · doi:10.3233/wor-2012-1408

Work organisation conditions, alcohol misuse: The moderating role of personality traits

2013· article· en· W1892869757 on OpenAlexaffabout
Sabine Saade, Alain Marchand

Bibliographic record

VenueWork · 2013
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyBig Five personality traitsPersonalityModerationSocial psychologyMultilevel modelPopulationWork (physics)Environmental healthMedicineEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: The moderating role of personality traits between work organization conditions and alcohol misuse by Canadian workers was examined. PARTICIPANTS: Longitudinal data came from Statistics Canada's National Population Health Survey (NPHS). METHODS: Data had a hierarchical structure and were analyzed using multilevel logistic regression models. RESULTS: The multilevel analyis revealed that skill utilisation at work increased by 7% the risk of being part of an alcohol misuse group. Similarly, psychological demands at work, and being confronted with an irregular work schedule increased alcohol misuse respectively by 69 and 611%. Inversely, workers confronted with a job insecurity and those benefitting from social support at work had a respective 12 and 5% lower risk of being part of an alcohol misuse group. As for personnality traits, self-esteem increased by 17% the risk of alcohol misuse, while sense of coherence decreased the risk by 1%. Finally, self-esteem moderated by 3% the impact of physical demands at work on workers'alcohol misuse. CONCLUSIONS: This study builds upon previous research, since no prior study was able to identify the moderating role that self-esteem plays between physical demands at work, and worker's alcohol misuse.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.361
Teacher spread0.327 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2013
Admission routes2
Has abstractyes

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