Annual Primary Care 2030 Convening: Creating an Enabling Ecosystem for Person-Centered Primary Healthcare Models to Achieve Universal Health Coverage in Low- and Middle-Income Countries
Bibliographic record
Abstract
Background: The 2019 United Nations General Assembly High-Level Meeting on Universal Health Coverage and the 2018 Declaration of Astana reaffirm the highest level of political commitment by United Nations Member States to achieve access to health services and primary healthcare for all. Both documents emphasize the importance of person-centered care in both healthcare services and systems design. However, there is limited consensus on how to build a strong primary healthcare system to achieve these goals. Methods: We convened a diverse group of global stakeholders for a high-level dialogue on how to create a person-centered primary healthcare system, using the country examples of the Republic of Kenya and the Socialist Republic of Vietnam. We focused our discussion on four themes to enable the creation of person-centered primary healthcare systems in Kenya and Vietnam: (1) strengthened community, person and patient engagement in subnational and national decision making; (2) improved service delivery; (3) impactful use of innovation and technology; and (4) meaningful and timely use of measurement and data. Findings: Here, we present a summary of our convening's proceedings, with specific insights on how to enable a person-centered primary healthcare system within each of these four domains. Conclusions: Following the 2019 United Nations General Assembly High-Level Meeting on Universal Health Coverage and the 2018 Declaration of Astana, there is high-level commitment and global consensus that a person-centered approach is necessary to achieve high-quality primary healthcare and universal health coverage. We offer our recommendations to the global community to catalyze further discourse and inform policy-making and program development on the path to Universal Health Coverage by 2030.
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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 | 0.031 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".