Willingness to take the Covid-19 vaccines and associated factors at a tertiary institution community in Johannesburg, South Africa
Bibliographic record
Abstract
Abstract Background SA is aiming to achieve herd immunity against Covid-19 by the first quarter of 2022. The success of the Covid-19 vaccination roll-out depends primarily on the willingness of the population to take the vaccines. Aim This study examined the willingness to take the Covid-19 vaccine, along with the factors of concern, efficacy, and preferences of the individual which may increase the willingness to be vaccinated. Setting This study was conducted at the University of the Witwatersrand, Johannesburg, amongst adult students and academic and professional staff. Methods We conducted a cross-sectional online study from 27 July – 14 August 2021. We performed descriptive and inferential analysis to determine the factors associated with willingness to take the Covid-19 vaccine. Results 2364 participants responded to a survey link and 82% were students, 66.8% were in the 18-29 years age band, females represented 64.0% and 49.2% were black people. 1965 (83.3%) were willing to receive a Covid-19 vaccine, the most preferred vaccines were Pfizer (41%) and J&J (23%), local pharmacy (29%) and GP (17%) were the preferred places for vaccination and the trusted sources of information on Covid-19 vaccines were the general practitioners (40.6%) and specialists (19,2%). Perceptions that vaccines are safe (aOR=31.56, 95%CI: 16.02-62.12 for affirmative agreement) and effective (aOR=5.92, 95%CI: 2.87-12.19 for affirmative agreement) were the main determinants for willingness to taking a COVID-19 vaccine Conclusion It is imperative to reinforce the message of Covid-19 vaccine safety and efficacy and to include the GPs and the community pharmacies in the vaccination roll-out in SA.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".