Vaccine Hesitancy, Coercion and Regret: Post-Pandemic Lessons on COVID-19 Vaccine Policies and Outreach among Newcomer Communities in Alberta, Canada
Bibliographic record
Abstract
Abstract Background Since the COVID-19 pandemic routine vaccination rates have dropped in Canada. Many newcomers and refugees experience significant vaccine inequities despite wide vaccine availability and COVID-19 pandemic vaccination campaigns. We aimed to investigate post-pandemic vaccine hesitancy, acceptance, and vaccine outreach strategies among newcomers’ communities. Methods We conducted a prospective community-based-participatory research (CBPR) qualitative study with self-identified newcomers in Alberta between October 2022 - January 2023. Community Scholars, leaders representing various ethnocultural communities trained in community-engaged research conducted semi-structured interviews and focus groups in English and first languages. Scholars collected and translated detailed qualitative notes and sociodemographic data with a standardized survey. Qualitative data was thematically analyzed and coded using a consensus-based approach. Results We conducted two focus groups and five semi-structured interviews with 12 participants, 50% who identified as female and originated from the Philippines, Ethiopia, Eritrea, Mexico, Burundi, and Egypt. We identified three main themes, each with two subthemes: (1) vaccine hesitancy due to lack of reliable information and religious and cultural beliefs, (2) access to COVID-19 vaccines and information, and (3) vaccine acceptance as voluntary or coerced. Employer mandated vaccination emerged as a critical issue with potential long-term negative public health implications, leading to vaccine regret and loss of trust of public health authorities and healthcare systems. Conclusion During population-wide COVID-19 vaccination campaigns newcomers perceived their communities’ circumstances were overlooked, potentially increasing vaccine hesitancy. Perceived coercive vaccination policies had unintended negative public health consequences. These findings may help inform future emergency and routine public health vaccination policies.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".