Sociodemographic and psychosocial factors associated with vaccine hesitancy – results from a longitudinal study in Singapore
Bibliographic record
Abstract
Singapore has one of the highest COVID-19 vaccination rates, however identifying vaccine-hesitant sub-groups and their concerns is vital given the need for future boosters in vulnerable populations. Furthermore, vaccine hesitancy remains a concern in the event of an emergence of a newer strain that necessitates the rolling out of a new vaccination programme. The aims of this study were to establish the extent of COVID-19 vaccine hesitancy and the factors influencing it among adults in Singapore using the Vaccine Hesitancy Scale (VHS). The study used a longitudinal methodology and participants were recruited in two waves from May 2020 to Sep 2022. In all 858 participants agreed to participate in both waves of the study. The two-factor structure of the VHS scale as established in earlier studies was tested using confirmatory factor analysis. The results revealed a two-factor structure of VHS comprising "lack of confidence" and "risks". Those who had higher stress, resilience, and concerns that they might be infected with COVID-19 at wave 1 were significantly associated with lower 'lack of confidence' scores i.e. lower vaccine hesitancy. In comparison, those with higher concerns about inadequate government preventive measures and unemployment at wave 1 were significantly associated with higher 'lack of confidence' scores. Those with higher concerns about inadequate government preventive measures in wave 1 were significantly associated with higher 'risks' scores i.e. higher vaccine hesitancy. The findings point toward the need for a nuanced messaging that considers the fears expressed by the populace and addresses them directly using clear simple language.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".