COVID‐19 vaccine hesitancy and attitudes in Qatar: A national cross‐sectional survey of a migrant‐majority population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it