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Record W2884498339 · doi:10.15171/ijoem.2018.1201

Association between the Increase in Body Mass Index and Medical Absenteeism in a Peruvian Mining Population

2018· article· en· W2884498339 on OpenAlexaff
Raúl Gómero, Ludy Murguía, Livia Calizaya, Christian R. Mejía, Arnaldo Sánchez-B

Bibliographic record

VenueThe International Journal of Occupational and Environmental Medicine · 2018
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsAbsenteeismBody mass indexMedicineDemographyOverweightObesityPopulationCohort studyGerontologyInternal medicineEnvironmental healthPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Obesity and overweight are associated with work absenteeism of medical cause. However, there is little knowledge on the relationship between incremental body mass index (BMI) and absenteeism. OBJECTIVE: To assess the effect of annual increase in BMI on amount of prolonged absenteeism. METHODS: Data from a longitudinal historical cohort of workers of a mining camp in Peru between 2006 and 2014 were used for the analysis. Prolonged absenteeism of 30 days or more in one year was chosen as the dependent variable; annual increase in BMI was considered as the explanatory variable. Regression analysis with generalized estimating equation was used to determine the relative risk adjusted for age, sex and type of work. RESULTS: There were 1347 cases of medical leave reported with a median of 6 days. Of all cases of medical leave, 11% of those who had an annual increase in BMI and 6% of those who maintained their BMI were cases of prolonged absenteeism. Prolonged absenteeism significantly increased in workers who had an annual increment in BMI (adj RR 1.16, 95% CI 1.05 to 1.29). CONCLUSION: The annual increase in BMI was marginally associated with prolonged absenteeism. Temporal increment in BMI, regardless of the baseline BMI, may be an independent determinant of the work absenteeism of medical cause.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.025
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.022
GPT teacher head0.369
Teacher spread0.347 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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