Multi-method findings on COVID-19 vaccine acceptability among urban refugee adolescents and youth in Kampala, Uganda
Bibliographic record
Abstract
Scant studies have explored COVID-19 vaccine acceptability among refugees. However, contexts of forced migration may elevate COVID-19 vulnerabilities, and suboptimal refugee immunisation rates are reported for other vaccine-preventable diseases. We conducted a multi-methods study to describe COVID-19 vaccine acceptability among urban refugee youth in Kampala, Uganda. This study uses cross-sectional survey data from a cohort study with refugees aged 16–24 in Kampala to examine socio-demographic factors associated with vaccine acceptability. A purposively sampled cohort subset (n = 24) participated in semi-structured in-depth individual interviews, as did key informants (n = 6), to explore COVID-19 vaccine acceptance. Among 326 survey participants (mean age: 19.9; standard deviation 2.4; 50.0% cisgender women), vaccine acceptance was low (18.1% reported they were very likely to accept an effective COVID-19 vaccine). In multivariable models, vaccine acceptance likelihood was significantly associated with age and country of origin. Qualitative findings highlighted COVID-19 vaccine acceptability barriers and facilitators spanning social-ecological levels, including fear of side effects and mistrust (individual level), misinformed healthcare, community and family attitudes (community level), tailored COVID-19 services for refugees (organisational and practice setting), and political support for vaccines (policy environment). These data signal the urgent need to address social-ecological factors shaping COVID-19 vaccine acceptability among Kampala’s young urban refugees.Trial registration: ClinicalTrials.gov identifier: 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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".