Patterns of follow-up mental health care after hospitalization for suicide-related behaviors among older adults in South Korea
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the sociodemographic and clinical factors associated with receiving follow-up mental healthcare within 7 days and 30 days after hospitalization for suicide-related behaviors (SRB) among older adults in South Korea. METHODS: Data from the Korean National Health Information Database were used, including information on sociodemographic variables and healthcare utilization. The study cohort consisted of individuals born in 1950 or before with a prior hospitalization record for suicide attempts or probable suicide attempts. Logistic regression analysis was conducted to predict the odds of receiving follow-up care within 7 days and 30 days, adjusting for covariates. RESULTS: Among the 37,595 older adults discharged from hospitalization for SRB, 29.13 % and 37.86 % received follow-up care within 7 days and 30 days, respectively. Follow-up care was more common among younger individuals, women, those with higher socio-economic status (SES), urban residents, and individuals with comorbidities. CONCLUSION: The provision of mental health follow-up care for older adults after hospitalization for suicide attempts is inadequate in South Korea. Increasing access to follow-up care among those with lower income, residing in rural areas, and older age is crucial. Public awareness campaigns, stigma reduction training for healthcare providers, and system-level changes, such as telemedicine and integrated care pathways, can help bridge the healthcare gap and reduce suicide mortality among older 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".