Developing and Testing Tele-Support Psychotherapy through Mobile Phones for Youth (15–30 Years) with Depression in Uganda
Bibliographic record
Abstract
In response to the rise in mental health problems among youth during the COVID-19 pandemic, we conducted a qualitative study in March 2022, using a user-centered design approach guided by ecological theories, to adapt group support psychotherapy into tele-support psychotherapy (TSP) via mobile phones. The development of a call platform, informed by the unified theory of acceptance and use of technology, was integrated into the Medical Concierge Group’s (TMCG) telehealth services under Rocket Health Africa. This platform included a dedicated toll-free line for psychotherapy, connecting users with lay counselors. An open-label randomized controlled trial (PACTR202201684613316) was conducted to assess the feasibility, acceptability, and effectiveness of TSP in combination with standard mental health services (n = 154) compared with standard mental health services alone (n = 146) among youth with mild to moderate major depression in Kampala. Participants commonly equated mental health with mental illness and reported significant challenges, including financial stress, substance abuse, and family dysfunction. Although digital interventions were largely accepted, some participants preferred in-person services. The adapted TSP maintained gender sensitivity and used folk tales, stories, riddles, and creative visualizations to facilitate emotional expression, acquisition of coping strategies, and income-generating skills, addressing both emotional and socio-economic needs.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".