Attitudes towards vaccines and intention to vaccinate against COVID-19: Implications for public health communications in Australia
Bibliographic record
Abstract
Abstract Objective To examine SARS-CoV-2 vaccine confidence, attitudes and intentions in Australian adults. Methods Nationwide survey in February-March 2021 of adults representative across sex, age and location. Vaccine uptake and a range of putative drivers of uptake, including vaccine confidence, socioeconomic status, and sources of trust, were examined using logistic and Bayesian regressions for vaccines generally and for SARS-CoV-2 vaccines. Results Overall 1,166 surveys were collected from participants aged 18-90 years (mean 52, SD of 19). Seventy-eight percent reported being likely to receive a vaccine against COVID-19. Higher SARS-CoV-2 vaccine intentions were associated with: increasing age (OR: 1.04 95%CI [1.03-1.044]), being male (OR: 1.37, 95% CI [1.08 – 1.72]), residing in the least disadvantaged area quintile (OR: 2.27 95%CI [1.53 – 3.37]) and a self-perceived high risk of getting COVID-19 (OR: 1.52 95% CI [1.08 – 2.14]). However, 72% of participants did not believe that they were at a high risk of getting COVID-19. Findings regarding vaccines in general were similar except there were no sex differences. For both the SARS-CoV-2 vaccine and vaccines in general, there were no differences in intentions to vaccinate as a function of education level, perceived income level, and rurality. Knowing that the vaccine is safe and effective, and that getting vaccinated will protect others, trusting the company that made it and getting vaccination recommended by a doctor were reported to influence a large proportion of the study cohort to uptake the SARS-CoV-2 vaccine. Seventy-eight percent reported the intent to continue engaging in virus-protecting behaviours (mask wearing, social distancing etc.) post-vaccine. Conclusions Seventy-eight percent of Australians are likely to receive a SARS-CoV-2 vaccine. Key influencing factors identified in this study (e.g. knowing that the vaccine is safe and effective, getting a doctor’s recommendation to get vaccinated) can be used to inform public health messaging to enhance vaccination rates. Strengths and limitations of this study This research captured a large, representative sample of the adult Australian population across age, sex, location, and socioeconomic status. We have self-reported Australian uptake intentions and attitudes on general vaccines and COVID-19 vaccine, and intent to continue engaging in virus-protecting behaviours (mask wearing, social distancing etc.) post SARS-CoV-2 vaccine. We examine a range of drivers and factors that may influence intent to get the SARS-CoV-2 vaccine uptake, including vaccine confidence, demographics and socioeconomic status. The survey is based on established behavioural theories, and is the Australian arm of the international iCARE survey which to date has collected global comparative information from over 90,000 respondents in 140 countries. Our survey was only available in English, which may have led to an underrepresentation of ethnic groups, and participation was voluntary, so our sample may be prone to selection bias from those with more interest or engagement in COVID-19.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".