Causation or selection – examining the relation between education and overweight/obesity in prospective observational studies: a meta‐analysis
Bibliographic record
Abstract
Numerous studies have investigated the association between education and overweight/obesity. Yet less is known about the relative importance of causation (i.e. the influence of education on risks of overweight/obesity) and selection (i.e. the influence of overweight/obesity on the likelihood to attain education) hypotheses. A systematic review was performed to assess the linkage between education and overweight/obesity in prospective studies in general populations. Studies were searched within five databases, and study quality was appraised with the Newcastle-Ottawa scale. In total, 31 studies were considered for meta-analysis. Regarding causation (24 studies), the lower educated had a higher likelihood (odds ratio: 1.33, 1.21-1.47) and greater risk (risk ratio: 1.34, 1.08-1.66) for overweight/obesity, when compared with the higher educated. However, these associations were no longer statistically significant when accounting for publication bias. Concerning selection (seven studies), overweight/obese individuals had a greater likelihood of lower education (odds ratio: 1.57, 1.10-2.25), when contrasted with the non-overweight or non-obese. Subgroup analyses were performed by stratifying meta-analyses upon different factors. Relationships between education and overweight/obesity were affected by study region, age groups, gender and observation period. In conclusion, it is necessary to consider both causation and selection processes in order to tackle educational inequalities in obesity appropriately.
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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 | 0.059 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".