The public's attitude to and acceptance of periodic doses of the COVID-19 vaccine: A survey from Jordan
Bibliographic record
Abstract
Aims This study surveyed people regarding their acceptance of periodic doses (i.e., annual boosters) of the COVID-19 vaccine. Moreover, factors that correlate with attitudes toward periodic COVID-19 vaccines were assessed and identified. Method The study employed a cross-sectional methodology. The study questionnaire was distributed using Google Forms. Data were collected during the last quarter of 2021, and 1,416 adults (18 years old and over) from Jordan responded. Acceptance of COVID-19 periodic vaccine doses was calculated as a percentage of the total number of study participants, and their attitudes were scored. A multiple regression model was used to determine the predictors of public attitudes toward the annual dose of COVID-19 vaccines. Results The acceptance rate for receiving periodic doses of the COVID-19 vaccine was low (19.3%). Additionally, 26% of participants were unsure about receiving additional doses of the vaccine. However, 54.7% had a negative attitude toward getting periodic doses. The mean score for attitudes toward periodic doses was 47.9 (range: 29–66). Among the identified factors leading to decisions not to receive periodic doses were side effects (49.1%), waiting for further clinical studies (38.8%), and perceived no risk of contracting COVID-19 (17.7%). Regression analysis showed that income, educational attainment, and following the news about COVID-19 were predictors of participants' attitudes toward the periodic COVID-19 vaccine. Conclusion Acceptance of periodic doses of the COVID-19 vaccine in Jordan is low, and the public's attitude is generally negative. Health programs and educational interventions are needed to promote vaccine acceptance and positive attitudes.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".