COVID‐19 vaccine hesitancy and attitudes in Qatar: A national cross‐sectional survey of a migrant‐majority population
Bibliographic record
Abstract
BACKGROUND: Vaccine hesitancy is a global threat undermining control of preventable infections. Emerging evidence suggests that hesitancy to COVID-19 vaccination varies globally. Qatar has a unique population with around 90% of the population being economic migrants, and the degree and determinants of hesitancy are not known. METHODS: This study was carried out to evaluate the degree of vaccine hesitancy and its socio-demographic and attitudinal determinants across a representative sample. A national cross-sectional study using validated hesitancy measurement tool was carried out from October 15, 2020, to November 15, 2020. A total of 7821 adults completed the survey. Relevant socio-demographic data along with attitudes and beliefs around COVID-19 vaccination were collected from the respondents. RESULTS: 20.2% of the respondents stated they would not take the vaccine and 19.8% reported being unsure about taking the prospective COVID-19 vaccine. Citizens and females were more likely to be vaccine hesitators than immigrants and males, respectively. Concerns around the safety of COVID-19 vaccine and its longer-term side effects were the main concerns cited. Personal research around COVID-19 and vaccine were by far the most preferred methods that would increase confidence in accepting the vaccine across all demographic groups. CONCLUSIONS: This study reports an overall vaccine hesitancy of 20% toward the COVID-19 vaccine and the influence of social media on attitudes toward vaccination which is in keeping with emerging evidence. This finding comes at a time that is close to the start of mass immunization and reports from a migrant-majority population highlighting important socio-demographic determinants around vaccine hesitancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".