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Record W3138715356 · doi:10.1177/1010539521998855

A Comparative Study of International and Asian Criteria for Overweight or Obesity at Workplaces in Singapore

2021· article· en· W3138715356 on OpenAlexaff
Nuraini Nazeha, Thirunavukkarasu Sathish, Michael Soljak, Gerard Dunleavy, Nanthini Visvalingam, Ushashree Divakar, Ram Bajpai, Chee Kiong Soh, George Christopoulos, Josip Car

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsMcMaster University
FundersNational Research Foundation Singapore
KeywordsOverweightObesityBody mass indexMedicineContext (archaeology)MalayPopulationEnvironmental healthEthnic groupCross-sectional studyDemographyGerontologyGeographyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

To compare the prevalence of and risk factors associated with overweight or obesity between the international (body mass index [BMI] ≥25 kg/m 2 ) and Asian (BMI ≥23 kg/m 2 ) criteria in a working population in Singapore. This was a cross-sectional analysis of a cohort study of 464 employees (aged ≥21 years) conducted at 4 workplaces in Singapore. The prevalence of overweight or obesity was 47.4% and 67.0% with the international and Asian criteria, respectively. With both the criteria, higher age, male sex, Malay ethnicity (vs Chinese), lower white rice intake, and consumption of sugar-sweetened beverages were positively associated with overweight or obesity. Participants with poorer mental health and higher levels of thermal comfort in the workplace were positively associated with overweight or obesity only with the Asian criteria. The use of international criteria alone in this population could have overlooked these risk factors that are highly relevant to the Singapore context.

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)0.002
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 score0.218
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.114
GPT teacher head0.412
Teacher spread0.297 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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