Leveraging the power of partnerships: spreading the vision for a population health care delivery model in western Kenya
Bibliographic record
Abstract
BACKGROUND: The Academic Model Providing Access to Healthcare (AMPATH) has been a model academic partnership in global health for nearly three decades, leveraging the power of a public-sector academic medical center and the tripartite academic mission - service, education, and research - to the challenges of delivering health care in a low-income setting. Drawing our mandate from the health needs of the population, we have scaled up service delivery for HIV care, and over the last decade, expanded our focus on non-communicable chronic diseases, health system strengthening, and population health more broadly. Success of such a transformative endeavor requires new partnerships, as well as a unification of vision and alignment of strategy among all partners involved. Leveraging the Power of Partnerships and Spreading the Vision for Population Health. We describe how AMPATH built on its collective experience as an academic partnership to support the public-sector health care system, with a major focus on scaling up HIV care in western Kenya, to a system poised to take responsibility for the health of an entire population. We highlight global trends and local contextual factors that led to the genesis of this new vision, and then describe the key tenets of AMPATH's population health care delivery model: comprehensive, integrated, community-centered, and financially sustainable with a path to universal health coverage. Finally, we share how AMPATH partnered with strategic planning and change management experts from the private sector to use a novel approach called a 'Learning Map®' to collaboratively develop and share a vision of population health, and achieve strategic alignment with key stakeholders at all levels of the public-sector health system in western Kenya. CONCLUSION: We describe how AMPATH has leveraged the power of partnerships to move beyond the traditional disease-specific silos in global health to a model focused on health systems strengthening and population health. Furthermore, we highlight a novel, collaborative tool to communicate our vision and achieve strategic alignment among stakeholders at all levels of the health system. We hope this paper can serve as a roadmap for other global health partners to develop and share transformative visions for improving population health globally.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".