Author comment: Findings from the Tushirikiane-4-MH (supporting each other for mental health) mobile health–supported virtual reality randomized controlled trial among urban refugee youth in Kampala, Uganda — R1/PR6
Bibliographic record
Abstract
Virtual reality (VR) for mental health promotion remains understudied in low-income humanitarian settings. We examined the effectiveness of VR in reducing depression with urban refugee youth in Kampala, Uganda. This randomized controlled trial assessed VR alone (Arm 1), VR followed by Group Problem Management Plus (GPM+) (Arm 2) and a control group (Arm 3), with a peer-driven and convenience sample of refugee youth aged 16–25 in Kampala. The primary outcome, depression, was measured with the Patient Health Questionnaire-9. Secondary outcomes included: mental health literacy, mental health stigma, self-compassion, mental well-being and adaptive coping. Analyses were conducted at three time points (baseline, 8 weeks, 16 weeks) using generalized estimating equations. Among participants (n = 335, mean age: 20.77, standard deviation: 3.01; cisgender women: n = 158, cisgender men: n = 173, transgender women: n = 4), we found no depression reductions for Arms 1 or 2 at 16 weeks compared with Arm 3. At 16 weeks, mental health literacy was significantly higher for Arm 2 compared with Arm 3, and self-compassion was significantly higher in Arm 1 and Arm 2 compared with Arm 3. VR alongside GPM+ may benefit self-compassion and MHL among urban refugee youth in Kampala, but these interventions were not effective in reducing depression.
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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 | 0.032 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 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".