Obesity status and sick leave: a systematic review
Bibliographic record
Abstract
This review identified 36 studies on the relation between obesity status and sick leave. Pooling of effect estimates was not possible due to great heterogeneity between studies regarding definition of sick leave (short-term/long-term), measure of obesity (body mass index/waist circumference/percentage body fat), definition of obesity status (World Health Organization standards/other), study population (sex/age/occupation/country) and exposure and outcome ascertainment (self-reported/objectively assessed). Nevertheless, a clear trend towards greater sick leave among obese compared with normal weight workers could be discerned, especially for spells of longer duration. In studies from the USA, which consistently reported about five times lower number of sick leave days per person-year than European, obese workers had about 1-3 extra days of absence per person-year compared with their normal weight counterparts. In European studies, the corresponding difference was about 10 d. For overweight workers the data were conflicting, indicating either increased or neutral level of sick leave compared with normal weight. Regarding underweight, the studies were very few and concerns regarding direction of causality were greater. Finally, in all four interventional studies identified substantial weight loss in obese subjects resulted in reduced sick leave, at least temporarily. In conclusion, increasing obesity in children and adults is likely to negatively affect future productivity as obesity increases the risk of sick leave, disability pension and death.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".