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Record W4393301799 · doi:10.7895/ijadr.435

Alcohol consumption and work-related health problems: Exploring the perceptions of Nigerian informal automobile artisans

2024· article· en· W4393301799 on OpenAlexvenueno aff
Funmilayo Juliana Afolabi

Bibliographic record

VenueThe International Journal of Alcohol and Drug Research · 2024
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
FundersTertiary Education Trust FundUniversiteit van Amsterdam
KeywordsAlcohol consumptionConsumption (sociology)PerceptionWork (physics)Environmental healthExcessive alcohol consumptionBusinessPsychologyAlcoholSociologyMedicineEngineeringChemistry

Abstract

fetched live from OpenAlex

Alcohol consumption is one of the leading factors that lead to work-related health problems worldwide. However, alcohol consumption among informal workers in developing countries is unknown. Using a qualitative method; this study explores the perception of the informal automobile artisans in Nigeria about alcohol consumption and its contribution to workplace health problems among the working population. Data were collected through in-depth interviews with a purposive sample of 43 automobile artisans from Osun State. Thematic analysis was done using MAXQDA 2020 software. The artisans identified a range of injuries and illnesses. They described the severity of these work-related health problems (WHPs) as either minor, serious or very serious. Moreover, the study shows the prevalence of alcohol consumption among the group. The artisans noted that alcohol consumption is one of the major causes of accidents and injuries among members. They further explained that members take alcohol to get strength for their tasks. The study concluded that using the workplace as a platform to address alcohol consumption is critical to promoting a healthy and productive work environment.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.119
GPT teacher head0.415
Teacher spread0.296 · 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
Published2024
Admission routes1
Has abstractyes

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