East Effort - A Community-Based Network of Ambassadors solve Health System Inequities in Toronto
Bibliographic record
Abstract
Background: Toronto equity deserving neighbourhoods often have poorer access to care and worse health outcomes than the rest of the city. Through a community-based network of Community Health Ambassadors (CHAs) we are addressing the root causes of these inequities and working together to create strong, healthy communities in east Toronto. Audience: Policy makers, funders, frontline organizations and community leaders will be interested to learn how working cross-sectorally, including with grassroots groups, can improve the health equity of a neighborhood. Approach: This interactive workshop will share stories from the frontlines on how to create a network of ambassadors supported by local community agencies and an Ontario Health Team (OHT). We will address issues of power, conflict of interest, collaborative decision making all by using a community-led framework. You will then practice applying these approaches in your local context and dialogue with the East Effort team to problem solve together. Outcomes: By the end of this workshop, you will have a deep understanding of the Community Health Ambassador model, some of the common challenges with implementation and potential solutions as well as ideas on how to implement a similar model in your community. The East Effort Community Health Ambassador Program has demonstrated some impressive annual outcomes: - Providing 6,87 referrals to community services - Providing 4,02 direct wrap-around supports - Providing education to ,00 clients to catch up on their cancer screening - Attaching 78 equity deserving community members to primary health care Client Testimonial: My experience was wonderful. I only spoke to the CHA over the phone after being referred by the Nurse Practitioner. She was very helpful and very accessible. She was willing to point me in to different directions. I really did appreciate that. Community ownership:The East Effort Community Health Ambassador Program is co-designed and co-implemented with community leaders. The Community Health Ambassadors (CHAs) are local champions who live in our neighbourhoods. They are paid staff who represent diverse community populations and speak many languages including Dari, Urdu, Slovak, Bengali, Arabic, Pashto, Amharic, Tamil and Hindi. The CHAs reported that: - 93% of them deepened connections with community members - 00% deepened trust with community organizations - 00% were involved in project design, execution and evaluation Equity: Health equity is at the core of the East Effort Community Health Ambassador Program. From our governance, which includes grassroots groups, community leaders and ambassadors , to our implementation, which involves hiring local champions into our organizations, we are enacting values of community-ownership and self-determination. Funds are given directly to grassroots groups to run programs and ambassadors can easily connect community members to services they need. East Effort is proud to have recently won an award highlighting the Transformative Change of our program: https://www.youtube.com/watch?v=YZokVOGHjQU
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".