Suicidal ideation and behavior in youth in low- and middle-income countries: A brief review of risk factors and implications for prevention
Bibliographic record
Abstract
Although global rates of suicide have dropped in the last 30 years, youth in low- and middle-income countries (LMICs) continue to be highly represented in suicide statistics yet underrepresented in research. In this review we present the epidemiology of suicide, suicidal ideation, and suicide attempts among youth in LMICs. We also describe population-level (attitudes toward suicide, socioeconomic, and societal factors) and individual-level clinical and psychosocial risk factors, highlighting specific considerations pertaining to youth in LMICs. These specific considerations in risk factors within this population can inform how multi-level prevention strategies may be targeted to meet their specific needs. Prevention and intervention strategies relying on the stepped-care framework focusing on population-, community-, and individual level targets while considering locally- and culturally relevant practices are key in LMICs. In addition, systemic approaches favoring school-based and family-based interventions are important among youth. Cross-culturally adapted multimodal prevention strategies targeting the heterogeneity that exists in healthcare systems, suicide rates, and risk factors in these countries should be accorded a high priority to reduce the burden of suicide among youth in LMICs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".