Association between the Increase in Body Mass Index and Medical Absenteeism in a Peruvian Mining Population
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".