Evaluating a mobile health intervention to increase COVID-19 prevention: engagement and learning outcomes among urban refugee youth in Kampala, Uganda
Bibliographic record
Abstract
Background: Displaced populations in resource-constrained settings require tailored COVID-19 prevention strategies, and mobile health (mHealth) emerges as a cost-effective approach. This study aimed to evaluate engagement and learning outcomes of the Kukaa Salama – Staying Safe – mHealth intervention for enhancing COVID-19 prevention practices among urban refugee youth. Method: This mixed-methods analysis used cross-sectional data from refugee youth aged 16–24 in Kampala, Uganda. Standardized questionnaires were used to collect mHealth engagement data and socio-demographic information. Participants shared learning experiences through responses to SMS check-ins and weekly informational messages. t -test, χ 2 , and Fisher’s exact tests were conducted to examine associations between mHealth engagement and socio-demographic factors. Inductive thematic analysis was employed to analyse qualitative responses related to learning experiences. Results: Among 346 participants (174 cisgender women, 166 cisgender men, 6 transgender individuals; mean age: 21.2, SD: 2.6), most reported using SMS services (84.8 per cent) while a lower proportion engaged in WhatsApp group chats (67.4 per cent). Participants who were older, born in Burundi and higher-educated were more likely to share in WhatsApp multimedia groups; those born in Burundi were also more likely to use SMS services. Four themes of learning outcomes emerged: COVID-19 self-protection strategies; awareness of the COVID-19 pandemic and relevant knowledge; significance of community and mutual support; self-efficacy and perseverance. Conclusions: Findings offer insights into characteristics of engagement and specific learning outcomes from the Kukaa Salama intervention. Future mHealth programmes can leverage community-based and age-sensitive approaches to enhance mental health support for young urban refugee.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".