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Record W3040267553 · doi:10.3233/wor-203200

Prevalence of abdominal obesity and associated lifestyle factors in bus drivers in a city in Southern Brazil

2020· article· en· W3040267553 on OpenAlexaboutno aff
Jonatan Candido da Silva, Mikael Seabra Moraes, Priscila Custódio Martins, Diego Augusto Santos Silva

Bibliographic record

VenueWork · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
Fundersnot available
KeywordsAbdominal obesityWaistObesityMedicineLogistic regressionOdds ratioAnthropometryConfidence intervalDemographyEnvironmental healthOddsGerontologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Bus drivers are exposed to unique working conditions. The behavioral and health trends of these workers should be investigated. OBJECTIVE: To estimate the prevalence of abdominal obesity and associated lifestyle factors in bus drivers in a city in Southern Brazil. METHODS: A cross-sectional study with 103 bus drivers with mean age of 41 years (±8.5) was conducted. Abdominal obesity was measured through waist circumference using anthropometric tape. To evaluate the different lifestyle domains, the Brazilian version of the Canadian Fantastic Lifestyle questionnaire was used. To check the association between abdominal obesity and lifestyle, binary logistic regression was used, with odds ratio (OR) estimates and 95% confidence intervals (95% CI). RESULTS: It was verified that 26.3% of drivers had abdominal obesity. In addition, bus drivers who had inadequate lifestyle in the "Nutrition" and "Type of behavior" domains were, respectively, 3.6 (95% CI: 1.3-9.5, p = 0.01) and 2.6 times (95% CI: 1.1-6.7; p = 0.04) more likely of having abdominal obesity when compared to those adequate in these lifestyle domains. CONCLUSIONS: Approximately one in four drivers had abdominal obesity and the "Nutrition" and "Type of Behavior" domains were associated with abdominal obesity.

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)0.000
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 score0.027
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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