Pilot towards developing a school mental health service: Experiences and lessons learnt in implementing Kenya integrated intervention model for dialogue and screening to promote children's mental well‐being
Bibliographic record
Abstract
AIM: This paper aims at documenting the process of implementing "Kenya Integrated intervention model for Dialogue and Screening to promote children's mental wellbeing (KIDS)", with emphasis on activities, experiences, challenges and lessons learnt through the process that can inform improvement in Kenya and other social-economically and culturally similar settings. METHODS: KIDS aimed at providing early and integrated culturally acceptable intervention to promote child mental health in 6 to 13 year olds, targeting 5429 children in 24 schools. It was a pilot study conducted in 2 administrative counties in Kenya (Machakos and Makueni). The main goal of this study was to test the efficacy and feasibility of the proposed model that incorporated several significant stakeholders. RESULTS: The implementation of KIDS was grouped into 6 stepwise and 2 ongoing processes: (1) Stakeholder engagement and collaboration; (2) Selecting, adapting, translating, piloting and adopting the study tools; (3) Conducting Participatory Theory of Change (ToC); (4) Building the capacity of the key implementing partners; (5) Community awareness and psycho-education, (6) Using school-based life skills and peer clubs counselling to provide psychosocial support; (7) Ongoing monitoring, supervision and quality assurance and (8) Ongoing knowledge exchange and dissemination. Specific logistical challenges and possible solutions for future school mental health services are also stated in this article. CONCLUSION: KIDS is a feasible model for school mental health service programme in Kenya but there are challenges which provide innovative opportunities for better designed school mental health services programme.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".