Exploring vaccination attitudes in African communities in Canada: A mixed-methods study protocol
Bibliographic record
Abstract
INTRODUCTION: Vaccine hesitancy is a complex issue influenced by many interacting factors. While literature on its contributing causes continues to expand, there is limited research on the contextual and cultural dynamics that shape vaccine hesitancy among African-born individuals in Canada. Identifying and understanding these factors is critical in developing targeted health interventions that address specific barriers to vaccination within this community. The study aims to explore the unique socio-cultural and context-specific elements of vaccine hesitancy among African community members living in Canada. METHODS AND ANALYSIS: The study will use a mixed-methods approach to investigate vaccine hesitancy among African community members living in Southwestern Ontario. In the qualitative study, we will conduct semi-structured interviews and participatory focus groups within each of the selected study areas: London, Windsor and Chatham-Kent. The qualitative data will be collected, transcribed and then analyzed thematically using NVivo 12. For the quantitative study, we will provide participants with surveys to accurately assess the predictors of vaccine hesitancy. The quantitative data will be analyzed using logistic regression to explore how socio-cultural influences, trust, and accessible information impact vaccine hesitancy. DISCUSSION: This study addresses a significant gap in existing literature by providing cultural and contextual insights on the drivers of vaccine hesitancy among African-born individuals. Using a mix-method design, the study offers a rich understanding of the influences shaping vaccine decision-making. The findings will support the development of health policies and interventions aimed at improving overall health outcomes for African communities within Canada.
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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 | 0.027 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".