Developing and testing tele-support psychotherapy using mobile phones for depression among youth in Kampala district, Uganda: study protocol for a pilot randomized controlled trial
Bibliographic record
Abstract
Introduction: In the post-COVID-19 era, depressive disorders among youth have risen significantly, creating an urgent need for accessible, cost-effective mental health interventions. This study adapts Group Support Psychotherapy into Tele-Support Psychotherapy (TSP) via mobile phones. It aims to evaluate its feasibility, acceptability, effectiveness, and cost-efficiency in addressing mild to moderate depression among youth in central Uganda. Methods and analysis: This study will use a mixed-methods approach, starting with a qualitative phase to adapt Group Support Psychotherapy into Tele-Support Psychotherapy (TSP) via mobile phones. Guided by ecological theories and the Unified Theory of Acceptance and Use of Technology (UTAUT), focus group discussions and interviews with youth, mental health professionals, and stakeholders will inform the development of a youth-tailored call platform integrated into Rocket Health Africa's telehealth services. Data will be analyzed using grounded theory and MAXQDA Analytics Pro 2022 to guide intervention adaptation. An open-label randomized controlled trial will enroll 300 youth (15-30 years) with mild to moderate depression from Kampala, Uganda, to evaluate Tele-Support Psychotherapy (TSP). Participants will be randomized to TSP with standard mental health services (SMHS) or SMHS alone. Primary outcomes include feasibility and acceptability, with secondary outcomes assessing cost-effectiveness, depressive symptom changes, and social support. Intention-to-treat analysis using structural equation modeling will evaluate treatment effects, complemented by qualitative insights into implementation barriers and facilitators. Discussion: This study protocol develops and evaluates Tele-Support Psychotherapy (TSP) for youth depression in resource-limited settings, addressing mental health gaps exacerbated by COVID-19. Using user-centered design and mixed methods, it explores TSP's feasibility, adaptability, and cost-effectiveness while addressing barriers like technology literacy, laying the groundwork for accessible digital mental health solutions. Trial Registration: PACTR202201684613316.
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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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.008 |
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".