Specialised Mental Health Care for Children in Humanitarian Settings: Integrating Local and Community-Owned Approaches
Bibliographic record
Abstract
Wars, armed conflict, climate crises, disasters and other humanitarian emergencies can threaten the mental health, wellbeing and development of children. Lack of specialised services for children with advanced mental health needs in humanitarian settings led to a large treatment gap. The Global Child Protection Area of Responsibility and Inter-Agency Standing Committee Mental Health and Psychosocial Support (IASC MHPSS) Reference Group commissioned this study to understand support pathways for children with advanced mental health needs in contexts with limited specialised services. A descriptive case study design across five humanitarian settings was used to (1) identify common themes in the implementation of specialised mental health services, (2) understand informal care pathways and (3) explore the integration of mental health psychosocial support (MHPSS) across sectors. Between 15 and 18 key informants were chosen at each country site for individual interviews. Interviews were conducted with global humanitarian MHPSS leaders and clinicians, child protection actors and community members. Results revealed common mental health issues and informal pathways children with serious mental health conditions rely on when services are limited. Cross-sectoral partnerships and collaboration with traditional and religious healers are vital components to strengthening local support for children’s mental health needs in humanitarian settings
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".