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Record W1968222128 · doi:10.1016/j.jegh.2014.08.001

Work productivity among adults with varied Body Mass Index: Results from a Canadian population-based survey

2014· article· en· W1968222128 on OpenAlexaffabout
Arnaldo Sanchez Bustillos, Kris Gregory Vargas, Raúl Gomero-Cuadra

Bibliographic record

VenueJournal of Epidemiology and Global Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPresenteeismAbsenteeismMedicineBody mass indexOverweightUnderweightDemographyObesityConfoundingLogistic regressionProductivityPopulationOdds ratioGerontologyEnvironmental healthPsychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The relationship between Body Mass Index (BMI) and work productivity, including absenteeism and presenteeism remains unclear. The objective of this study was to examine work productivity among adults with varied BMI using population-based data. METHODS: Data source was the 2009-2010 Canadian Community Health Survey. The outcomes reflected work absence (absenteeism) and reduced activities at work (presenteeism). The key explanatory variable was BMI in six categories. Logistic regressions were used to measure the association between outcome and explanatory variables adjusting for potential confounders. RESULTS: The sample consisted of 56,971 respondents ranging in age from 20 to 69 years. Relative to normal BMI, the odds of absenteeism were higher for those in the obesity class III (OR=1.60, 95% CI: 1.39; 1.83). Presenteeism was weakly associated with all obesity categories (OR=1.49, 95% CI: 1.38; 1.61, for obesity class I). Overweight was marginally associated with absenteeism and presenteeism. Underweight was inversely associated with absenteeism. CONCLUSIONS: This study found that obesity is an independent risk factor for reduced work productivity. Both absenteeism and presenteeism were associated with obesity. However, being overweight was weakly associated with work productivity.

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)0.023
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.032
GPT teacher head0.382
Teacher spread0.350 · 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.

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

Citations51
Published2014
Admission routes2
Has abstractyes

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