SECONDARY DATA ANALYSES OF THE PREDICTORS OF DEPRESSION TREATMENT OUTCOMES, MECHANISMS OF GROUP SUPPORT PSYCHOTHERAPY IN THE SEEK-GSP TRIAL AND TRANSLATING FINDINGS THROUGH AN UPGRADED ONLINE TRAINING
Bibliographic record
Abstract
This project builds on the SEEK-GSP trial, a cluster-randomized controlled trial conducted in northern Uganda that demonstrated the effectiveness and cost-effectiveness of Group Support Psychotherapy (GSP) for treating depression among people living with HIV. The proposed work has three main aims: Identify predictors of early treatment non-response to depression therapy in HIV-positive populations by analyzing trial data with advanced predictive modeling techniques. Elucidate mechanisms of change by conducting causal mediation analyses to determine how GSP reduces depression—focusing on mediators such as coping skills, social support, stigma reduction, engagement in income-generating activities, and reduced alcohol use. Translate findings into practice by upgrading the SEEK-GSP Academy, a digital training platform for lay health workers, ensuring they are equipped with evidence-based tools and tailored guidance to deliver GSP effectively at scale. Ethical approvals and strong stakeholder engagement (including TASO and Uganda’s Ministry of Health) underpin the project. By combining rigorous secondary analysis with digital capacity building, this initiative aims to improve mental health and HIV outcomes in sub-Saharan Africa while advancing scalable, culturally adapted psychotherapeutic interventions
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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 | 0.042 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".