Implementing High-Quality Primary Care Through a Health Equity Lens
Bibliographic record
Abstract
The COVID-19 pandemic highlighted the importance of centering health equity in future health system and primary care reforms. Strengthening primary care will be needed to correct the longstanding history of mistreatment of First Nations/Indigenous and racialized people, exclusion of health care workers of color, and health care access and outcome inequities further magnified by the COVID-19 pandemic. The National Academies of Sciences, Engineering, and Medicine (NASEM) released a report on Implementing High-Quality Primary Care: Rebuilding the Foundation of Health Care, that provided a framework for defining high-quality primary care and proposed 5 recommendations for implementing that definition. Using the report’s framework, we identified health equity challenges and opportunities with examples from primary care systems in the United States and Canada. We are poised to reinvigorate primary care because the recent pandemic and the attention to continued racialized police violence sparked renewed conversations and collaborations around equity, diversity, inclusion, and health equity that have been long overdue. The time to transition those conversations to actionable items to improve the health of patients, families, and communities is now. Appeared as Annals “Online First” article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".