The role of grassroots community organizations in transforming healthcare systems to achieve health equity
Bibliographic record
Abstract
Two events converged in early 2020 to expose vast disparities and inequities that have been harming many racialized and marginalized people for decades and shake up healthcare systems around the world. The COVID-19 pandemic and the brutal killing by police of George Floyd, an unarmed Black man, created a groundswell of emotions that erupted in protests and calls for social justice. Governments, corporations, and businesses made statements against racism, primarily anti-Black racism, instituted Diversity, Equity, and Inclusion (DEI) policies, and appointed DEI managers to show they were taking action to tackle racism and uphold social justice. However, grassroots community organizations had the most impact in mobilizing populations and effecting change to contain and reduce the spread of the deadly COVID-19 virus, as well as challenging leaders to do more than just talk about dismantling systemic, structural, and institutional racism.
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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 | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".