Examining the barriers, facilitators and attitudes towards COVID-19 vaccine and public health measures for black communities in Canada: a qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: Black communities claim the highest number of cases and deaths due to COVID-19 in Canada. Generating culturally/contextually appropriate public health measures and strategies for vaccine uptake in black communities within Canada can better support the disproportionate impact of this pandemic. This study explores the barriers and enablers to public health measures limited to mask-wearing, disinfection, sanitation, social distancing and handwashing, as well as the barriers and attitudes towards COVID-19 vaccines among the black community. METHODS AND ANALYSIS: We will use qualitative approaches informed by the widely accepted Consolidated Framework for Implementation Research (CFIR) to aid our investigation. We will conduct 120 semistructured interviews and five focus groups with black populations across the major provinces of Canada to understand the barriers and facilitators to public health measures, including barriers and attitudes towards COVID-19 vaccines. Data will be organised and analysed based on the CFIR. Facilitators and barriers to COVID-19 preventative measures and the barriers, facilitators and attitudes towards COVID-19 vaccines will be organised to explore relationships across the data. ETHICS AND DISSEMINATION: This study was approved by the Social Sciences, Humanities and Education Research Ethics Board at the University of Toronto (41585). All participants are given information about the study and will sign a consent form in order to be included; participants are informed of their right to withdraw from the study. Research material will be accessible to all researchers involved in this study as no personal identifiable information will be collected during the key informant semistructured interviews and focus groups. The study results will be provided to participants and published in peer-reviewed journals.
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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 | 0.039 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".