Virtual Aunting and Public Health Emergencies: A Novel Approach to Sharing Public Health Guidance
Bibliographic record
Abstract
Effective communication with the public is essential during health emergencies. As evident during the coronavirus disease 2019 (COVID-19) pandemic, the lack of effective public health communication with equity-deserving groups has contributed to higher morbidity and mortality than the non-racialized community. This concept paper will describe a grassroots community effort to provide culturally safe public health information to the East African community in Toronto at the beginning of the pandemic. Community members collaborated with The LAM Sisterhood to create a virtual aunt, Auntie Betty, and record voice notes with essential public health guidance in Swahili and Kinyarwanda. This manner of communicating with the East African community was well-received and has shown great potential as a tool to support effective communication efforts during public health emergencies that disproportionately impact Black and equity-deserving communities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".