Effectiveness of barber-facilitated “Doing What Matters in Times of Stress” intervention among urban literate youths in Western Kenya: A cluster randomised trial
Bibliographic record
Abstract
Youth with mental health disorders are a growing concern in Kenya, yet services are inaccessible. "Doing What Matters in Times of Stress (DWM)" is a scalable self-help intervention that can be delivered by lay providers. However, its effectiveness in low-resource, non-humanitarian settings is unknown. We evaluated the effectiveness of barber-facilitated DWM intervention in reducing psychological distress, while improving functioning and resilience among literate urban youths in Western Kenya. This cluster randomised trial involved 15 barbers in intervention group and 15 in the waiting-list control. Barbers were from 30 barbershops in Bungoma and Kitale towns, aged 18-30 years and literate in English. Using mental health-themed music as an entry point, barbers recruited youth aged 18-29 years, fluent in English, and had mild or moderate psychological distress and functioning difficulties. Intervention participants received DWM guide and/or audios, supported by barbers through three individual sessions. Waiting-list control participants received the intervention after five weeks. Outcome measures included Patient Health Questionnaire (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7), Perceived Stress Scale (PSS-10) and psychological outcome profiles (PSYCHLOPS). We analysed data using cumulative ordinal multilevel models. Between 31st July 2023-27th January 2024, 330 eligible youths were enrolled (n = 158, from 11 intervention arm barbers and n = 172 from 10 control barbers). Post-intervention, intervention group youth had 95%, 82% and 71% lower odds of higher depression (AOR = 0.05, 95% CI 0.02-0.10, p < 0.001), anxiety (AOR = 0.18, 95% CI 0.09-0.34, p < 0.001) and stress (AOR = 0.29, 95% CI 0.12-0.71, p = 0.007), respectively. They also had 86% lower odds of higher self-identified problem severity scores (AOR = 0.14, 95% CI = 0.08 -0.27, p < 0.001) and 4.3 times higher odds of higher resilience levels (AOR = 4.33, 95% CI 2.00-9.36, p < 0.001). DWM effectively reduced psychological distress and improved resilience among youths. Barbershops are promising community places for mental health promotion. Long-term, multi-site studies are required to assess outcome sustainability and scalability. Trial registration: Pan African Clinical Trials Registry PACTR202306502042812.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.008 | 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".