Kukaa Salama (Staying Safe): study protocol for a pre/post-trial of an interactive mHealth intervention for increasing COVID-19 prevention practices with urban refugee youth in Kampala, Uganda
Bibliographic record
Abstract
INTRODUCTION: With over 82.4 million forcibly displaced persons worldwide, there remains an urgent need to better describe culturally, contextually and age-tailored strategies for preventing COVID-19 in humanitarian contexts. Knowledge gaps are particularly pronounced for urban refugees who experience poverty, overcrowded living conditions and poor sanitation access that constrain the ability to practise COVID-19 mitigation strategies such as physical distancing and frequent hand washing. With over 1.4 million refugees, Uganda is sub-Saharan Africa's largest refugee hosting nation. More than 90 000 of Uganda's refugees live in Kampala, most in informal settlements, and 27% are aged 15-24 years old. There is an urgent need for tailored COVID-19 responses with urban refugee adolescents and youth. This study aims to evaluate the effectiveness of an 8-week interactive informational mobile health intervention on COVID-19 prevention practices among refugee and displaced youth aged 16-24 years in Kampala, Uganda. METHODS AND ANALYSIS: We will conduct a pre-test/post-test study nested within a larger cluster randomised trial. Approximately 385 youth participants will be enrolled and followed for 6 months. Data will be collected at three time points: before the intervention (time 1); immediately after the intervention (time 2) and at 16-week follow-up (time 3). The primary outcome (self-efficacy to practise COVID-19 prevention measures) and secondary outcomes (COVID-19 risk awareness, attitudes, norms and self-regulation practices; depression; sexual and reproductive health practices; food and water security; COVID-19 vaccine acceptability) will be evaluated using descriptive statistics and regression analyses. ETHICS AND DISSEMINATION: This study has been approved by the University of Toronto Research Ethics Board, the Mildmay Uganda Research Ethics Committee, and the Uganda National Council for Science & Technology. The results will be published in peer-reviewed journals, and findings communicated through reports and conference presentations. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04631367).
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.070 | 0.012 |
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".