Breaking the silence on suicide among pre-adolescent children
Bibliographic record
Abstract
Globally, the rising burden of suicide among pre-adolescent children is a pressing clinical problem for mental health practitioners. Over 90% of the world's youth live in low- and middle-income countries, where suicide is the second-leading cause of death among children and youth. Literature about completed suicide in Ugandan children below 10 years of age is limited, although there is a growing body of research predominantly from high-income countries. We present the only available literature in Uganda about pre-adolescent suicide as reported in press-media reports. We utilized the multilevel risk framework to discuss the multi-sociocultural perspective regarding child rearing, the role of childhood trauma, the evolving digital environment, and legal and policy frameworks. We discussed challenges to the practice of Child and Adolescent psychiatry in Uganda, where childhood mental health disorders continue to receive limited attention in clinical practice. We recommend future research efforts to develop a robust methodology to better understand pre-adolescent suicide. Implementation of actionable interventions like school-based suicide screening, community gatekeeper trainings, and child helplines are key. Intersectoral collaborations among diverse stakeholders are essential for co-creating actionable and evidence-based preventive interventions that place the community at the centre.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".