Social selection in historical time: The case of tuberculosis in South Korea after the East Asian financial crisis
Bibliographic record
Abstract
The perspectives of social selection and causation have long been debated. Social selection theory is as "social" as social causation theory, since all diseases are social and no biological process occurs outside society. To identify the social selection pathway and historical juncture affected by socioeconomic and political changes, we investigated the reciprocal impact of suffering from tuberculosis (TB) on the current socioeconomic position (SEP), stratified by childhood SEP. We also examined the extent to which the social consequences of ill health changed since the East Asian economic downturn. Data were collected for 2007-2012 from the Korea National Health and Nutritional Examination Survey. To identify associations between TB history and current household income (HHI), we constructed an ordinal logistic regression model adjusted for covariates, including age, gender, educational attainment, and job status. We adopted a recursive regression model to examine trend changes in this association from 1980-2012 to 2003-2012. Of 28,136 participants, 936 had experienced TB. In the first ordinal logistic regression, the TB group was more likely to have lower HHI than the non-TB group. The odds ratios (ORs) increased from 1.30 (1980-2012) to 1.86 (2003-2012) for the TB group, increasing their probability of having low HHI. Among the low childhood SEP group, the TB group's probability of having low HHI was 1.35 (95% confidence interval [CI]: 1.16-1.57) during 1980-2012, which increased to 2.01 (95% CI: 1.37-2.95) during 2003-2012. For the high childhood SEP group, the TB group's OR range fluctuated, similar to that for the non-TB group. The results support the social selection pathway from TB history to adverse impact on current SEP. Our study identified downward social mobility due to TB history among the low childhood SEP group. Moreover, negative social consequences deteriorated since the East Asian economic crisis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".