Addressing sociodemographic disparities in COVID-19 vaccine uptake among youth in Zimbabwe
Bibliographic record
Abstract
INTRODUCTION: COVID-19 vaccine acceptance research has mostly originated from high-income countries and reasons why youth may not get vaccinated may differ in low-income settings. Understanding vaccination coverage across different population groups and the sociocultural influences in healthcare delivery is important to inform targeted vaccination campaigns. METHODS: A population-based survey was conducted in 24 communities across three provinces (Harare, Bulawayo and Mashonaland East) in Zimbabwe between October 2021 and June 2022. Youth aged 18-24 years were randomly selected using multistage sampling. Sociodemographic characteristics, COVID-19 vaccination uptake and reasons for non-uptake were collected, and odds of vaccination was investigated using logistic regression. RESULTS: 17 682 youth were recruited in the survey (n=10 742, 60.8% female). The median age of participants was 20 (IQR: 19-22) years. Almost two thirds (n=10 652, 60.2%) reported receiving at least one dose of COVID-19 vaccine. A higher proportion of men than women had been vaccinated (68.9% vs 54.7%), and vaccination prevalence increased with age (<19 years: 57.5%, 20-22: 61.5%, >23: 62.2%). Lack of time to get vaccinated, belief that the vaccine was unsafe and anxiety about side effects (particularly infertility) were the main reasons for not getting vaccinated. Factors associated with vaccination were male sex (OR=1.69, 95% CI 1.58 to 1.80), increasing age (>22 years: OR=1.12, 95% CI 1.04 to 1.21), education level (postsecondary: OR=4.34, 95% CI 3.27 to 5.76) and socioeconomic status (least poor: OR=1.32, 95% CI 1.20 to 1.47). CONCLUSION: This study found vaccine inequity across age, sex, educational attainment and socioeconomic status among youth. Strategies should address these inequities by understanding concerns and tailoring vaccine campaigns to specific groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".