A Comprehensive Approach to Medical Oxygen Ecosystem Building: An Implementation Case Study in Kenya, Rwanda, and Ethiopia
Bibliographic record
Abstract
Medical oxygen is an essential treatment for life-threatening hypoxemic conditions and is commonly indicated for the clinical management of most leading causes of mortality in children aged younger than 5 years, obstetric complications at delivery, and surgical procedures. In resource-constrained settings, access to medical oxygen is unreliable due to cost, distance from production centers, undermaintained infrastructure, and a fragmented supply chain. To increase availability of medical oxygen in underserved communities, Assist International, the GE Foundation, Grand Challenges Canada, the Center for Public Health and Development (Kenya), Health Builders (Rwanda), and the National Ministries of Health and Regional Health Bureaus in Kenya, Rwanda, and Ethiopia partnered to implement a social enterprise model for the production and distribution of medical oxygen to hospitals at reduced cost. This model established pressure swing adsorption (PSA) plants at large referral hospitals and equipped them to serve as localized supply hubs to meet regional demand for medical oxygen while using revenues from cylinder distribution to subsidize ongoing costs. Since 2014, 4 PSA plants have successfully been established and sustained using a social enterprise model in Siaya, Kenya; Ruhengeri, Rwanda; and Amhara Region, Ethiopia. These plants have cumulatively delivered more than 209,708 cylinders of oxygen to a network of 183 health care facilities as of October 2022. In Ethiopia, this model costs an estimated US$7.34 per patient receiving medical oxygen over a 20-year time horizon. Altogether, this business model has enabled the sustainable provision of medical oxygen to communities with populations totaling more than 33 million people, including an estimated 5 million children aged younger than 5 years.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".