Contextualization and adaptation of the child and adolescent mental and behavioural disorders module of the mhGAP-IG in Kilifi and Nairobi counties in Kenya
Bibliographic record
Abstract
The Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) was developed by the World Health Organization as a key tool for delivering evidence-based mental healthcare in non-specialized settings. The mhGAP-IG requires contextualization and adaptation to ensure local relevance. However, evidence on adapting the Child and Adolescent Mental Disorders (CMH) module of the mhGAP-IG is limited. This study contextualized and adapted the 2016 mhGAP-IG CMH module through two workshops with local mental health experts and stakeholders, preceded by six in-depth interviews exploring the child and adolescent mental health contexts in Nairobi and Kilifi. Data were analysed in NVivo-Lumivero© software. Interviews with mental health stakeholders revealed significant challenges in both counties, including a shortage of mental health specialists, frequent medication stockouts, stigma and inadequate resources. Key adaptations to the module included using locally acceptable terms (e.g., replacing 'failure to thrive' with 'suboptimal growth'); expanding training to five days; adding the mhGAP-IG Essential Care and Practice module to address culturally sensitive communication in mental healthcare provision; streamlining referral pathways; and incorporating aspects of self-harm/suicide and substance use linked to the CMH module content. Contextualizing the CMH module is crucial for effective implementation, but sustaining impact will require addressing systemic barriers beyond capacity-building.
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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 | 0.004 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".