Proactive Strategies to Address Health Equity and Disparities: Recommendations from a Bi-National Symposium
Bibliographic record
Abstract
INTRODUCTION: Health inequities persist in Canada and the United States. Both countries show differential health status and health care quality by social characteristics, making zip or postal code a greater predictor of health than genetics. Many social determinants of health overlap in the same individuals or communities, exacerbating their vulnerability. Many of the contributing factors and problems are structural and evade simple solutions. METHODS: In March 2017 a binational Canada-US symposium was held in Washington DC involving 150 primary care thought leaders, including clinicians, researchers, patients, and policy makers to address transformation in integrated primary care. This commentary summarizes the session's principal insights and solutions of the session tackling health inequities at policy and delivery levels. DISCUSSION: The solution lies in intervening proactively to reduce disparities-developing risk-adjustment measures that integrate social factors; increasing the socioeconomic, racial, and ethnic diversity of health providers; teaching cultural humility; supporting community-oriented primary care; and integrating equity considerations into health system funding. We propose moving from retrospective analysis to proactive measures; from equality to equity; from needs-based to strength-based approaches; and from an individual to a population focus.
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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 | 0.057 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.039 | 0.047 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".