Measuring Socioeconomic Inequalities in Obesity among Korean Adults, 1998–2015
Bibliographic record
Abstract
Obesity is a prominent global public health challenge as its prevalence has grown. Even though the increase in prevalence of obesity in Korea has been relatively low, it is expected to continually increase in the next several years, leading to social and economic burdens. This study aimed to assess socioeconomic inequalities in obesity among Korean adults. Using nationally representative survey datasets, the concentration index (CI) and decomposition of the CI were used to capture and quantify obesity-related inequalities from 1998 to 2015. The results suggested that pro-poor inequalities in obesity existed in Korea, indicating that obesity was more concentrated among individuals with lower income. In a gender-stratified model, obesity was more concentrated among women with lower income and men with higher income, showing that the trend and magnitude of inequalities in obesity each vary by gender. The decomposition approach revealed that, over the past 17 years, the main contributors to the existing inequalities were higher education and higher income levels. These findings suggest that comprehensive and multifaceted interventions at the local and national levels should be considered to address the identified income- and education-related barriers with respect to obesity among Korean adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".