A community health volunteer delivered problem-solving therapy mobile application based on the Friendship Bench ‘Inuka Coaching’ in Kenya: A pilot cohort study
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Bibliographic record
Abstract
BACKGROUND: Sub-Saharan Africa (SSA) has the largest care gap for common mental disorders (CMDs) globally, heralding the use of cost-cutting approaches such as task-shifting and digital technologies as viable approaches for expanding the mental health workforce. This study aims to evaluate the effectiveness of a problem-solving therapy (PST) intervention that is delivered by community health volunteers (CHVs) through a mobile application called 'Inuka coaching' in Kenya. METHODS: A pilot prospective cohort study recruited participants from 18 health centres in Kenya. People who self-screened were eligible if they scored 8 or higher on the Self-Reporting Questionnaire-20 (SRQ-20), were aged 18 years or older, conversant in written and spoken English, and familiar with the use of smart mobile devices. The intervention consisted of four PST mobile application chat-sessions delivered by CHVs. CMD measures were administered at baseline, 4-weeks (post-treatment), and at 3-months follow-up assessment. RESULTS: = 22) completed their 4-week assessments, and 52 participants completed their 3-month follow-up assessment. The results showed a significant improvement over time on the Self-Reporting Questionnaire-20 (SRQ-20). Higher-range income, not reporting suicidal ideation, being aged over 30 years, and being male were associated with higher CMD symptom reduction. CONCLUSION: To our knowledge, this report is the first to pilot a PST intervention that is delivered by CHVs through a locally developed mobile application in Kenya, to which clinically meaningful improvements were found. However, a randomised-controlled trial is required to robustly evaluate this intervention.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it