Individual Factors Influencing the Public’s Perceptions About the Importance of COVID-19 Immunity Certificates in the United Kingdom: Cross-sectional Web-based Questionnaire Survey
Bibliographic record
Abstract
BACKGROUND: Understanding how perceptions around immunity certificates are influenced by individual characteristics is important to inform evidence-based policy making and implementation strategies for services around immunity and vaccine certification. OBJECTIVE: This study aimed to assess what were the main individual factors influencing people's perception of the importance of using COVID-19 immunity certificates, including health beliefs about COVID-19, vaccination views, sociodemographics, and lifestyle factors. METHODS: A cross-sectional web-based survey with a nationally representative sample in the United Kingdom was conducted on August 3, 2021. Responses were collected and analyzed from 534 participants, aged 18 years and older, who were residents of the United Kingdom. The primary outcome measure (dependent variable) was the participants' perceived importance of using immunity certificates, computed as an index of 6 items. The following individual drivers were used as the independent variables: (1) personal beliefs about COVID-19 (using constructs adapted from the Health Belief Model), (2) personal views on vaccination, (3) willingness to share immunity status with service providers, and (4) variables related to respondents' lifestyle and sociodemographic characteristics. RESULTS: The perceived importance of immunity certificates was higher among respondents who felt that contracting COVID-19 would have a severe negative impact on their health (β=0.2564; P<.001) and felt safer if vaccinated (β=0.1552; P<.001). The prospect of future economic recovery positively influenced the perceived importance of immunity certificates. Respondents who were employed or self-employed (β=-0.2412; P=.001) or experienced an increase in income after the COVID-19 pandemic (β=-0.1287; P=.002) perceived the use of immunity certificates as less important compared to those who were unemployed or had retired or those who had experienced a reduction in their income during the pandemic. CONCLUSIONS: The findings of our survey suggest that more vulnerable members in our society (those unemployed or retired and those who believe that COVID-19 would have a severe impact on their health) and people who experienced a reduction in income during the pandemic perceived the severity of not using immunity certificates in their daily life as higher.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".