Development and Evaluation of a Peer Equity Navigator Intervention for COVID-19 Vaccine Promotion and Uptake in African, Caribbean and Black Communities in Ottawa, Canada
Bibliographic record
Abstract
Abstract Background African, Caribbean and Black Communities (ACB) have experienced an increased burden of COVID-19 morbidity and mortality as well as significant barriers to COVID-19 vaccine acceptance and uptake. Addressing the complex issues of vulnerable populations, such ACB communities, requires a multipronged approach and innovation. Peer-led approaches framed within critical health literacy (CHL) and critical racial literacy (CRL) discourses, along with collaborative and participatory equity learning processes, increased community capacity, empowerment, and practice outcomes. They may contribute to long-term improvements in health and health equity. Methods We developed and evaluated a peer-equity navigator intervention to increase vaccine confidence and acceptance in ACB communities using a modified Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) Framework. The evaluation drew upon multiple data sources, including tracking data, surveys with community members, and a focus group with peer equity navigators (PENs). Results We found that an innovative, community-informed and peer-led model designed to increase awareness and agency among ACB communities was feasible, acceptable, and effective for over 1500 ACB community members between Sept 16, 2022 and Jan 28, 2023. Consistent with the partnership approach, 8 trained PENS conducted over 56 community events. Peer equity navigators (PENS) and community members reported high levels of engagement, appreciation for peer-led community-based approaches and increased vaccine literacy. Conclusions The PEN approach is a feasible, acceptable, and effective intervention for reaching and engaging ACB community members in health-promoting actions and behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".