Bibliographic record
Abstract
<h3>Introduction</h3> The outbreak of novel COVID-19 caught the world off guard in the first quarter of 2020. To stem the tide of this pandemic, there was acceleration of the development, testing and prelicensure approval for emergency use of some COVID-19 vaccine candidates. This led to raised public concern about their safety and efficacy, compounding the challenges of vaccine hesitancy. The onus of managing and administering these vaccines to a sceptical populace when they do become available rests mostly on the shoulders of healthcare workers (HCWs). Therefore, the vaccine confidence levels of HCWs become critical to the success of vaccination endeavours. This proposed study aims to estimate the level of vaccine confidence and the intention to receive a COVID-19 vaccine among future HCWs and their trainers at a specific university in Cape Town, South Africa, and to identify any vaccination concerns early for targeted intervention. <h3>Methods and analysis</h3> This proposed study is a cross-sectional survey study. An online questionnaire will be distributed to all current staff and students of the Faculty of Medicine Health Sciences of Stellenbosch University in Cape Town, South Africa. No sampling strategy will be employed. The survey questionnaire will consist of demographic questions (consisting of six items) and vaccine confidence questions (comprising six items in Likert scale format). Log binomial models will be employed to identify factors associated with vaccine confidence and intention. The strength of association will be assessed using the OR and its 95% CI. Statistical significance will be defined at a p value <0.05. <h3>Ethics and dissemination</h3> Ethics approval has been obtained for the study from Stellenbosch University (Human Research Ethics Committee reference number S19/01/014 (PhD)). The results will be shared with relevant health authorities, presented at conferences and published in a peer-reviewed journal.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".