Examining the effects of social assistance on suicide-related behaviour among impoverished older adults in Korea using a nationwide cohort study from 2010–2019
Bibliographic record
Abstract
Suicide rates among older adults in Korea are one of the highest in the world. Although prior research found that poverty is associated with suicide mortality among older adults in Korea, it is unknown whether being a recipient of social assistance may reduce suicide risk among older adults living in poverty. We examined the impact of the public assistance program on suicide-related behaviour (SRB). Data from a nationwide cohort in the Korean National Health Insurance Database, including demographic and medical treatment information, linked to the vital statistics for 2010-19 was used. The entire Korean population born before 1951 was included (n = 7,889,086). Flexible parametric survival model was performed to assess whether the risk of SRB hospitalisation and death differed across a) different levels of socioeconomic status, and b) social assistance status among the poor, using gender-stratified models. Older age, chronic disease, poverty, and being out of the labour market were associated with higher SRB hospitalisation and death for both genders. Among those living in poverty, social assistance recipients had lower rates for SRB death and hospitalisation, compared to the low-income non-recipients. Men who were non-recipients had 0.21 higher hazards ratio (95%CI 0.20 to 0.22) for SRB deaths compared to recipients, and the difference was 0.11 (95%CI 0.10 to 0.11) in women, with similar patterns for SRB hospitalizations. Poverty was associated with higher SRB death and hospitalisation among older adults in Korea. Social assistance targeting the older population may help reduce SRB.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".