COVID-19 vaccination in high-risk communities: Case study of Brampton, Ontario
Bibliographic record
Abstract
Background: The Peel region in Southern Ontario is among the most ethnoculturally diverse and fastest growing areas in Canada. During the COVID-19 pandemic, the multicultural community of Brampton suffered one of the highest infection rates in Canada, in part because of the demographic and socioeconomic characteristics of the community. The role of pharmacists in supporting vaccine uptake in this linguistically, ethnically and religiously diverse community has not been adequately characterized. Methods: A qualitative case study approach was used, focusing on one of the major communities in Peel (Brampton). Interviews with community pharmacists and pharmacy staff directly involved in COVID-19 vaccine administration during the pandemic were undertaken to identify common experiences and trends related to providing care and support to this high-risk community. Constant comparative coding was used to identify common themes that can inform ongoing public health supports in future pandemics. Results: A total of 29 interviews were completed. Key themes that emerged included 1) the impact of operational, organizational and logistical issues on vaccine uptake in the community; 2) the negative influence of inconsistent messaging from public health and other experts during the pandemic; and 3) the identification of an emerging typology of "vaccine hesitancies" describing different reasons/motivations for avoiding COVID-19 vaccination and approaches taken by pharmacy staff to address these within a multicultural, multilingual practice context. Discussion: The COVID-19 vaccination campaign was unprecedented in its size, scope and speed, and community pharmacists were integral in this effort. The unique needs of ethnoculturally, linguistically and socioeconomically diverse communities like Brampton require further studies to examine ways in which the pharmacy profession can positively influence greater vaccine uptake, by increasing understanding of the diverse proliferation of vaccine hesitancies that emerged.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".