Feasibility study on the <scp>mhGAP‐IG</scp> as a tool to enhance parental awareness of symptoms of mental disorders in lower primary (6‐10 year old) school‐going children: Towards inclusive child mental health services in a Kenyan setting
Bibliographic record
Abstract
AIMS: (1) To determine the feasibility of involving parents as key partners in school mental health activities; (2) to determine whether educating parents on mental health treatment Gap Intervention Guideline (mhGAP-IG) section on children leads to enhanced parent perception of mental health symptoms in their children; and (3) to determine context appropriate social demographic predictors of the parental awareness following the psychoeducation using the mhGAP-IG children version. METHODS: Consenting parents completed the Child Behaviour Checklist (CBCL) and the Brief Problem Monitor for Parents (BPM-P) about their children at baseline and at 6 months post-baseline respectively. Immediately after post-baseline, they received psychoeducation on the importance of mental well-being in children and how to recognize symptoms of mental disorders, using the mhGAP-IG section on children. This psychoeducation was the intervention between baseline and 6 months post-baseline. We analysed means of mental disorder symptoms and prevalence of the various mental syndromes/problems on the CBCL/BPM-P scores to determine the statistical significance of the changes between baseline and 6 months. RESULTS: Overall, there was significant increase (P < 0.05) in the symptoms mean scores and prevalence of syndromes/problems between baseline and 6 months post-psychoeducation. However, there were some differences between urban and rural settings and in some parents and children socio-demographics and gender that should be considered in individual cases. CONCLUSION: It is feasible to include parents in school mental health programmes as key stakeholders. The mhGAP-IG section on children is a good tool for psychoeducation. However, there are predictors of outcomes that need further research.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".