A Mobile-Based Preventive Intervention for Young, Arabic-Speaking Asylum Seekers During the COVID-19 Pandemic in Germany: Design and Implementation
Bibliographic record
Abstract
BACKGROUND: Most individuals seeking asylum in Germany live in collective housing and are thus exposed to a higher risk of contagion during the COVID-19 pandemic. OBJECTIVE: In this study, we aimed to test the feasibility and efficacy of a culture-sensitive approach combining mobile app-based interventions and a face-to-face group intervention to improve knowledge about COVID-19 and promote vaccination readiness among collectively accommodated Arabic-speaking adolescents and young adults. METHODS: We developed a mobile app that consisted of short video clips to explain the biological basis of COVID-19, demonstrate behavior to prevent transmission, and combat misconceptions and myths about vaccination. The explanations were provided in a YouTube-like interview setting by a native Arabic-speaking physician. Elements of gamification (quizzes and rewards for solving the test items) were also used. Consecutive videos and quizzes were presented over an intervention period of 6 weeks, and the group intervention was scheduled as an add-on for half of the participants in week 6. The manual of the group intervention was designed to provide actual behavioral planning based on the health action process approach. Sociodemographic information, mental health status, knowledge about COVID-19, and available vaccines were assessed using questionnaire-based interviews at baseline and after 6 weeks. Interpreters assisted with the interviews in all cases. RESULTS: Enrollment in the study proved to be very challenging. In addition, owing to tightened contact restrictions, face-to-face group interventions could not be conducted as planned. A total of 88 participants from 8 collective housing institutions were included in the study. A total of 65 participants completed the full-intake interview. Most participants (50/65, 77%) had already been vaccinated at study enrollment. They also claimed to comply with preventive measures to a very high extent (eg, "always wearing masks" was indicated by 43/65, 66% of participants), but practicing behavior that was not considered as effective against COVID-19 transmission was also frequently reported as a preventive measure (eg, mouth rinsing). By contrast, factual knowledge of COVID-19 was limited. Preoccupation with the information materials presented in the app steeply declined after study enrollment (eg, 12/61, 20% of participants watched the videos scheduled for week 3). Of the 61 participants, only 18 (30%) participants could be reached for the follow-up interviews. Their COVID-19 knowledge did not increase after the intervention period (P=.56). CONCLUSIONS: The results indicated that vaccine uptake was high and seemed to depend on organizational determinants for the target group. The current mobile app-based intervention demonstrated low feasibility, which might have been related to various obstacles faced during the delivery. Therefore, in the case of future pandemics, transmission prevention in a specific target group should rely more on structural aspects rather than sophisticated psychological 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".