Mobilizing community engagement for crisis response: lessons learned from a COVID-19 mass vaccination clinic in Cobourg, Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Communities have an important role to play in disaster and emergency planning and response. Yet community members are not typically engaged in official planning activities, including plans for mass immunization clinics during infectious disease emergencies, such as the COVID-19 pandemic. This qualitative study explores one case of a community driven effort to implement a COVID-19 mass vaccination clinic in Cobourg, Ontario, Canada. Operational between mid-March 2021 and late February 2022, the Cobourg Community Centre (CCC) clinic involved 600 community volunteers, and at its peak completed approximately 700 vaccinations a day. The development and operation of the clinic was largely grassroots, spearheaded by local non-profits and volunteers. Drawing on insights from the various actors involved, this study seeks to understand the factors that made this collaborative effort a success. METHODS: Semi-structured interviews and focus groups were conducted between September 2022 and July 2023 with 34 individuals involved in coordinating and operating the CCC mass vaccination clinic including volunteer community members, members of local community organizations and businesses, public health unit and hospital staff and city employees. Data was analyzed utilizing an inductive thematic analysis. RESULTS: Four major themes were identified that contributed to the clinic's success and enabled the team's ability to navigate challenges including, a collaborative model, leveraging community knowledge and networks, flexibility and autonomy, and volunteers as an asset. CONCLUSION: The findings of this study indicate the importance of community engagement in strengthening emergency planning and response for future public health emergencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".