Utilizing Community Health Ambassadors to improve COVID-19 vaccine uptake in equity priority neighborhoods in Toronto, Canada
Bibliographic record
Abstract
East Toronto Health Partners (ETHP) is comprised of 100+ health and social organizations, with a leadership council consisting of patient/caregivers and six anchor organizations representing the continuum of care. During the pandemic ETHP implemented a Community Health Ambassador (CHA) Model to increase access to COVID19 vaccinations in underserved neighborhoods through health promotion activities and referral to COVID19 outreach Centers (CoCs). The intervention focuses on equity priority neighborhoods with significant concentrations of underserved populations including but not limited to newcomers and households with low incomes. Early in the COVID-19 pandemic it became apparent that the impact was disproportionally high in equity priority neighbourhoods. Once COVID19 vaccine became available to the public, it became evident that the wealth of knowledge residing within the community was essential to improve vaccine uptake, particularly in priority neighbourhoods. ETHP partnered with the community to co-design outreach strategies. A particularly meaningful partnership was the collaboration with community members who assisted us in designing our CHA model. The CHAs program was administered through one of the community ETHP health service providers. CHAs were localized to the neigbourhood where they lived and utilized their knowledge of the community to identify and address misinformation and barriers to vaccination. CHAs were able to engage in individualized educational interventions locally and escalate systemic barriers to the ETHP. These issues were addressed through rapid cycle change quality improvement methodology by the members of the ETHP. From December, 2021 to March, 2022, we engaged and deployed 197 CHAs to conduct outreach activities. They reached out to over 25,000 individuals in our priority neighbourhoods. Their work bridged the equity gap in vaccination. In September 2021, there was a 20% point difference in 1st dose coverage between the most vaccinated and least vaccinated neighborhoods in East Toronto; in March 2022, this difference decreased to 16% partly as the result of CHA outreach in priority neighborhoods. Barriers to vaccine uptake could best be identified by hyper-local strategies implemented by CHA with a deep understanding of the local neighborhood. The barriers identified by CHAs could be addressed with the help of an integrated health system to improve equitable delivery of COVID-19 vaccine. Based on the success of the CHA model were are co-designing a strategy to utilize CHAs to assist with health care recovery focusing on other preventive interventions that not equitably delivered such as cancer screening.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".