Improving Equity Through Primary Care: Proceedings of the 2019 Toronto International Conference on Quality in Primary Care
Bibliographic record
Abstract
Health equity allows people to reach their full health potential and receive high-quality care that is appropriate for them and their needs, no matter where they live, what they have, or who they are. It is a core element of quality in health care. Around the world, there are many efforts to improve equity through primary care. In order to advance these efforts, it is important to share successes and challenges. Building on our work with international stakeholders to identify key primary care research priorities, we organized the Toronto International Conference on Quality in Primary Care that was held on November 16, 2019. Participants from 8 countries took part. Key recommendations included the establishment of continuous relationships between providers and patients over time, relationships between providers in the health and social sectors, and resources supported proportionally to patient need. Solutions must be generated using team-based approaches that explicitly include people with who have experienced discrimination. Progress will require confronting structural determinants including racism, capitalism, and colonialism. Conference participants suggested practical solutions, such as developing a public transportation program for rural residents to improve community building and the ability to attend medical appointments, and identifying patients who have recently missed clinic visits that may benefit from additional care. These approaches will need to be evaluated through high-quality research and quality improvement, with a knowledge translation that facilitates sustainability and expansion across settings.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".