Development of prediction models of COVID-19 vaccine uptake among Lebanese and Syrians in a district of Beirut, Lebanon: a population-based study
Bibliographic record
Abstract
Introduction: Vaccines are essential to prevent infection and reduce the morbidity of infectious diseases. Previous evidence has shown that migrants and refugees are particularly vulnerable to exclusion and discrimination, and low COVID-19 vaccine intention and uptake were observed among refugees globally. This study aimed to develop and internally validate prediction models of COVID-19 vaccine uptake by nationality. Methods: This is a nested prognostic population-based cross-sectional analysis. Data were collected between June and October 2022 in Sin-El-Fil, a district of Beirut, Lebanon. The study population included a random sample of Lebanese adults and all Syrian adults residing in areas of low socioeconomic status. Data were collected through a telephone survey. The main outcome was the uptake of at least one dose of the COVID-19 vaccine. Predictors of COVID-19 vaccine uptake were assessed using the Least Absolute Shrinkage and Selection Operator regression for Lebanese and Syrian nationalities in separate models. Results: Of 2028 participants, 79% were Lebanese, 18% Syrians and 3% of other nationalities. COVID-19 vaccination uptake was higher among Lebanese (85% (95% CI 82% to 86%) compared to Syrians (47% (95% CI 43% to 51%)) (p<0.001); adjusted OR 6.2 (95% CI 4.9 to 7.7). Predictors of uptake of one or more COVID-19 vaccine doses for Lebanese were older age, presence of an older adult in the household, higher education, greater asset-based wealth index, private healthcare coverage, feeling susceptible to COVID-19, belief in the safety and efficacy of vaccines and previous receipt of the influenza vaccine. For Syrians, predictors were older age, male sex, completing school or higher education, receipt of cash assistance, presence of chronic illness, belief in the safety and efficacy of vaccines, previous receipt of the influenza vaccine and possession of a legal residency permit in Lebanon. Conclusions: These findings indicate barriers to vaccine uptake among Syrian refugees and migrants, including legal residency status. These findings call for urgent action to enable equitable access to vaccines by raising awareness about the importance of vaccination and the targeting of migrant and refugee populations through vaccination campaigns.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".