A community health volunteer delivered problem-solving therapy mobile application based on the Friendship Bench ‘Inuka Coaching’ in Kenya: A pilot cohort study
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.005 | 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".