A Rapid Primary Healthcare Response to COVID-19: An Equity-Based and Systems- Thinking Approach to Care Ensuring that No One Is Left Behind
Bibliographic record
Abstract
Recent data from across the globe show that COVID-19 is disproportionately affecting those who are already adversely impacted by social determinants of health. In this paper, we explore how members of the Alliance for Healthier Communities - comprehensive, salary-based primary care organizations in Ontario - anticipated the same and rapidly responded by adapting their services to ensure continued equitable access to primary care services. Lessons from this project could be adapted in other primary care team-based models or partnerships to ensure ongoing support for populations that are most at risk from COVID-19 and the consequences of restricted access to services.
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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 | 0.036 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".