Fertility concerns and COVID-19 Vaccines: Community-informed infographic design in urban Waterloo Region, Ontario, Canada
Bibliographic record
Abstract
Introduction: Vaccine hesitancy, including concerns about possible fertility side-effects, caused delay in the uptake of COVID-19 vaccines in Canada and elsewhere. One way of tackling vaccine hesitancy is the use of infographics that explain key issues and address concerns. The aim of this study was to explore the collaborative process of rapidly developing an infographic that was informed by community feedback and tailored to address fertility concerns during urgent COVID-19 pandemic conditions. Methods: A survey promoted through social media and focus group discussion with community contacts were used to iteratively consult target audiences and gather feedback on interpretation of the infographic’s content and meaning. Survey results were analysed using descriptive methods. A focus group discussion was analysed using inductive thematic and sentiment analysis. Feedback guided infographic development. Results: A draft infographic and survey were shared online. 33 of 37 survey respondents expressed that they trusted the information provided in infographics. Survey respondents and focus group participants both wanted simple language and additional information to address concerns about the long-term effect of COVID-19 vaccines on fertility. Opinions indicated that more effort was needed to address varying levels of health literacy within communities. There was conflicting feedback on whether use of inclusive language by removing gender labels and focusing on biology, was helpful or confusing. Conclusions: This study shows public feedback can help tailor content and design of vaccine confidence building tools making them more accessible to the general population. In addition, efforts to resolve specific concerns can be augmented by modifying and/or creating different versions of infographics.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".