Socioeconomic Inequalities in the Continuum of Suicidal Ideation
Bibliographic record
Abstract
Abstract: Background: Suicidal ideation changes over time. Few studies have examined socioeconomic inequalities in suicide ideation regarding interrelated indicators of incidence, persistence, and remission. Aim: We aimed to investigate how socioeconomic characteristics are related to the incidence, persistence, and remission of suicidal ideation in South Korea. Methods: We assessed the incidence of suicidal ideation at the 1-year follow-up among individuals without prior suicidal ideation and its persistence and remission at the 2-year follow-up. We conducted a multinomial logistic regression analysis to examine the association between SES and three indicators of the course of suicidal ideation. Results: Among 8,334 respondents, the 12-month incidence of suicidal ideation was 2.1%. Among these cases, 18.5% persisted, while 81.5% remitted. Low SES, particularly income, was strongly associated with the incidence, persistence, and remission of suicidal ideation. The socioeconomic association was strongest for persistent suicidal ideation (adjusted odds ratio [aOR]: 3.66; 95% confidence interval [CI]: 1.47–9.10), followed by incidence (aOR: 1.59; 95% CI: 1.10–2.29) and remission (aOR: 1.32; 95% CI: 0.88–1.97) of suicidal ideation. Limitations: We could not examine changes in these risk factors during the follow-up period. Conclusion: Adverse socioeconomic circumstances may precipitate the worsening in the course of suicidal ideation. Addressing the socioeconomic determinants of suicidal ideation should be prioritized.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".