The Friendship Bench programme: a cluster randomised controlled trial of a brief psychological intervention for common mental disorders delivered by lay health workers in Zimbabwe
Bibliographic record
Abstract
BACKGROUND: Common mental disorders (CMD) are a leading cause of disability globally. Emerging evidence indicates that in low and middle income countries the treatment gap for CMD can be addressed through the use of trained and supervised lay health workers (LHWs). Few clinical trials have evaluated the use of such task-shifting approaches in sub-Saharan Africa. In Zimbabwe, we have successfully piloted a task-shifting intervention delivered by LHWs. This protocol describes a cluster randomised controlled trial to assess the effectiveness of this intervention. METHODS: Each of 24 randomly selected clinics from a pool of 42 in Harare will recruit 24 participants (N = 576). The clinics are randomised in a 1:1 ratio to receive either the intervention package [a problem solving therapy package delivered over a 4-6 week period by LHWs (N = 24) followed by a 6-week group support programme which focuses mainly on teaching a craft skill] or enhanced usual care, which includes usual care and psycho-education. Primary care attenders aged 18 years and above who score positive on a locally validated CMD screening questionnaire (Shona Symptom Questionnaire, SSQ-14) will be eligible for recruitment and asked for informed consent to participate in the trial. The primary measure is the SSQ score at 6 months. CONCLUSION: This effectiveness trial using LHWs to address the treatment gap for CMD will contribute to the body of knowledge on the feasibility and ability for scale-up of interventions for CMD. TRIAL REGISTRATION: PACTR201410000876178.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".