ARIE: A Health Equity Framework for Public Health Interventions Informed by Critical Race Theory and Critical Gerontology
Bibliographic record
Abstract
Older racialized minorities were particularly vulnerable during the last pandemic due to the interlocking influences of structural racism and ageism, which are often disregarded in public health planning. This oversight not only compromises the social justice and health equity goals of public health efforts but it also calls for a more inclusive approach that systematically addresses these deficiencies at every stage of a public health response. To achieve this, we propose Age- and Race-conscious Interventions done Equitably (ARIE), a novel analytical framework grounded in critical race theory and critical gerontology. ARIE is based on a four-step approach, which aligns with different stages of public health interventions. It will help ensure that structural discrimination influencing access to healthcare resources during a biological event is not ignored, and that public health authorities work actively toward identifying and addressing ageist and racist biases in their response plans and interventions.
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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 | 0.060 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".