Community organization perspectives on COVID-19 vaccine hesitancy and how they increased COVID-19 vaccine confidence: a Canadian Immunization Research Network, social sciences and humanities network study
Bibliographic record
Abstract
Background: COVID-19 vaccines play a critical role in reducing the morbidity and mortality associated with SARS-CoV-2 infection and despite vaccine availability, disparities in COVID-19 vaccine uptake among Canadian subgroups exist. Community organizations are uniquely situated to relay important vaccine messaging around all vaccines, understand components of vaccine hesitancy, and facilitate vaccine uptake within the communities they serve. The objective of this research was to solicit community organizations perspectives specific to COVID-19 vaccines and explore strategies of increasing vaccine uptake within their communities. Methods: A qualitative focus group study was held in the spring of 2021 with 40 community organizations from across the country. Discussions focused on COVID-19 vaccine communication and awareness within their communities, vaccine misinformation, and strategies to increase vaccine acceptance and access. Data were analyzed utilizing thematic and inductive techniques. Results: Vaccine hesitancy was identified among staff and clients. Vaccine confidence, complacency, convenience, and mistrust in government and authorities were identified as contributors to vaccine hesitancy. Community organizations utilized innovative and novel methods to encourage vaccine uptake and increase vaccine confidence. Leveraging established trusting relationships was key to successful messaging within communities. Conclusion: Community organizations used innovative methods, built on established trust, to increase vaccine confidence within their communities and among their staff. Community agencies played an important role in COVID-19 vaccine uptake within subgroups of the Canadian population. Community organizations are key public health partners and play a critical role in increasing COVID-19 vaccine confidence.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".