World Health Organization life‐skills training is efficacious in reducing youth self‐report scores in primary school going children in Kenya
Bibliographic record
Abstract
AIM: Documented evidence shows that training in life skills for school going children improves their physical and mental health status. Although Kenya has a curriculum and a policy for life-skills training in schools, these have not been implemented because lack of local evidence for efficacy. Therefore, the objective of this study was to determine the efficacy and effectiveness of the life-skill training curriculum for primary schools developed by the Ministry of Education, Kenya. METHODS: We randomly selected 23 schools from two randomly selected sub-counties representing urban/peri-urban and rural contexts in Kenya. We collected baseline socio-demographic characteristics and administered the locally validated youth self-report (YSR) for 11 to 18 years old at baseline and 9 months post-intervention. We used the Ministry of Education validated curriculum for life-skills training for upper primary school as the intervention immediately after the baseline. RESULTS: The World Health Organization life-skills training is efficacious in reducing YSR scores in primary school going children in Kenya. We found that socio-demographic characteristics were predictors for mental health and that there were significant positive improvements in internalizing and externalizing YSR symptoms and syndromes in both sites. The improvement was over 40% and therefore unlikely to be attributable to placebo effect or natural recovery without intervention. However, attention problems worsened more so in rural sites and particularly in girls. CONCLUSIONS: Life-skills training is efficacious in improving mental health in school going children in the Kenyan context. However, it is not effective in attention problems which seem to be cognitive in nature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".