"Hear Me to Know What Matters": Insights to Action Addressing Inequities of COVID-19 Care Experienced by the Socially Vulnerable
Bibliographic record
Abstract
It has been said that COVID-19 places us in the same storm, but in different boats. High River meat packing plant COVID19 outbreak in April 2020 is a study on how socio-ethno-economic factors can play a vital role in COVID-19 spread. 1,560 cases were linked to the plant with 936 employees (out of 1,700) testing positive. Many meatpacking plant workers are newly arrived immigrants and temporary foreign workers. An innovative care pathway (ECIP) emerged to rapidly close the existing gap between primary care and social support agencies to successfully mitigate the spread. Nearly 2,163 households were addressed. 98% of COVID+ employees never saw acute care. Co-designed recruitment strategies with patient advisors played a vital role in gaining trust among impacted communities to share their socio-clinical care experiences. Crucial insights from 41 patient stories were a significant motivator in harnessing the collective potential through collaboration between Government of Alberta, Primary Care Networks and Alberta Health Services in arranging on-site vaccinations at 5 meatpacking plants in the Calgary Zone. Over 3,000 workers were efficiently mass vaccinated in a span of 7 days. Patient experience was overwhelmingly positive. Importantly, ease of vaccine access is trending to be a factor that may positively impact vaccine hesitancy.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.012 |
| 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".