34 COVID-19, vaccination, and trust: an interview study
Bibliographic record
Abstract
In addition to direct health threats of the COVID-19 pandemic, societies are experiencing significant harms and burdens associated with measures to mitigate the effects of the virus. In this context, a possible vaccine is perhaps the most highly regarded prospect to combat the novel coronavirus and enable societies to lift COVID related restrictions. Governments and other institutions around the world have invested large amounts of resources into the development, testing, and production capacity for several different vaccines. When vaccines become available, public health authorities will need information about the concerns and decision-making considerations of constituents. We outline here the key findings from interviews with residents of Ontario, Canada, (n=40) in July and August of 2020 regarding their views, concerns, and intentions with respect to a prospective COVID-19 vaccine. In particular, participants expressed concern about the safety and efficacy of any prospective vaccine that is developed in a short timeframe, despite eagerness to eventually take it. Additionally, participants expressed considerations that, while not directly related to vaccines, nevertheless factored into their attitudes about accepting a possible COVID vaccination. These included how successful governments have managed COVID-19 so far, existing relationships with healthcare providers, and how they have assessed their risk of contracting or becoming very ill from COVID-19. Trust in science, regulators, and governments will play a critical role in the successful deployment of a COVID-19 vaccine. Governments and public health institutions can take actions to earn trust. Implementing monitoring programs for long-term adverse effects would measure and potentially mitigate risk of unforeseen effects. Supports that provide financial and social stability during the wait for rigorously tested vaccines may increase trust in governments to act in the best interest of their constituents.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".