Addressing COVID-19 Vaccine Distrust Among Black Patients: The LEAPS Framework & Afrocentric Approaches in Medical Education
Bibliographic record
Abstract
Context: Physicians can significantly influence a patient9s decision to vaccinate against SaRS-CoV-2 virus. Black people in Canada are at high risk of covid infections, hospitalizations, ICU admissions and deaths, yet are more hesitant to receive the vaccine. But despite being among those most affected by the COVID-19 pandemic, only 56.4% of Black Canadians report being willing to receive a COVID-19 vaccine compared to 76.9% of the overall population. Vaccine hesitancy in Black communities is not merely due to misinformation; it is also linked to medical distrust and systemic racism. However, medical education often lacks training on specific strategies for communicating with vaccine hesitant patients from Black communities. Study Design: Community-based participation and medical education program development. Objectives: Based on previous successes in employing Afrocentric approaches in primary care clinics to increase influenza vaccine uptake among vaccine-hesitant Black patients, ongoing collaboration with community practitioners and health centres serving Black communities in the Toronto area aimed to create a better understanding of Afrocentric health promotion approaches, through the development of: (1) a 9LEAPS9 of care communication framework, (2) and an educational online module involving a vaccine-hesitant Black patient that describes in a role-playing simulation how to apply Afrocentric approaches and LEAPS communication strategies in clinical practice. Intervention: A clinical case and a webpage including three-part video series, less than 10 minute each, was developed to improve providers’ comfort in discussing vaccine distrust and anti-Black racism in healthcare with Black patients. Results: In collaboration with the Ontario Medical Association, the Black Physicians Association of Ontario, the TAIBU community health centre and the University of Toronto Department of Family and Community Medicine, we created and disseminated an online educational module to help improve clinicians, residents and medical students9 counselling skills regarding vaccine distrust and anti-Black racism in healthcare. This module is transferable to an UGME, PGME, CME, and CPD model of training. This project has shown that it is feasible to use Black community-engagement to maximize medical education and train physicians and collaborative care providers. Future directions involve designing a study for intervention evaluation and implementation.
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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 | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".