Lost-time illness, injury and disability and itsrelationship with obesity in the workplace:A comprehensive literature review
Bibliographic record
Abstract
The objective of this study was to conduct a literature review examining predictors of lost-time injury, illness and disability (IID) in the workplace, with a focus on obesity as a predictor, and to evaluate the relationship between obesity and losttime IID. The study objective was also to analyze workplace disability prevention and interventions aimed at encouraging a healthy lifestyle among employees and reducing obesity and IID, as well as to identify research gaps. The search was conducted in several major online databases. Articles included in the review were published in English in peer-reviewed journals between January 2003 and December 2014, and were found to be of good quality and of relevance to the topic. Each article was critically reviewed for inclusion in this study. Studies that focused on lost-time IID in the workplace were reviewed and summarized. Workers in overweight and obese categories are shown to be at a higher risk of workplace IID, are more likely to suffer from lost-time IID, and experience a slower recovery compared to workers with a healthy body mass index (BMI) score. Lost-time IID is costly to an employer and an employee; therefore, weight reduction may financially benefit both - workers and companies. It was found that some companies have focused on developing interventions that aid reduction of weight and the practice of active lifestyle among their employees. Int J Occup Med Environ Health 2016;29(5):749-766.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".