Trends in Gender Differences in Self-Rated Health in Korea: Evidence from the Korean National Health and Nutrition Examination Survey, 2001-2012
Bibliographic record
Abstract
Despite rapid economic growth during the last half of the twentieth century, gender inequality has remained high in Korea. Using data from the 2001 Korean National Health and Nutrition Examination Survey (KNHANES), previous research found that gender differences in sociostructural and psychosocial factors contributed to a substantial female excess in poor self-rated health. To the extent that women’s overall social status relative to men has improved over time in Korea, it is possible that the gender gap in perceived health has decreased. This study used repeated cross-sectional KNHANES data from 2001-2012 to examine temporal trends in gender differences in self-rated health. In age-adjusted models, we found no significant trend in the female excess of poor self-rated health among respondents aged 25-44 (p=0.685). In contrast, we found a statistically significant downward trend among those aged 45-64 (p<0.001). In fully adjusted models controlling for age and behavioral, sociostructural, and psychosocial covariates, we found a marginally significant upward trend (p=0.08) among younger respondents, while the downward trend among older respondents remained significant (p<0.001). More work is needed to determine why gendered health disparities decreased among older adults in Korea but not among those aged 25-44.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".