An Oxygen Supply Is Not Enough: A Qualitative Analysis of a Pressure Swing Adsorption Oxygen Plant Program in Ethiopian Hospitals
Bibliographic record
Abstract
BACKGROUND: In response to critical gaps in medical oxygen access, 2 pressure swing adsorption (PSA) oxygen production centers were established using an ecosystem-strengthening strategy in Amhara, Ethiopia, in 2019. A qualitative study was conducted to assess enablers and bottlenecks to oxygen access at the hospital level after installation. METHODS: A variety of hospital staff (clinicians, biomedical professionals, hospital administrators, and procurement teams) across 13 hospitals procuring oxygen from the plants participated in comprehensive, semistructured focus group discussions. A thematic framework analysis approach was used to identify key themes. FINDINGS: A total of 101 individuals participated in 26 focus groups in 2021, 2 years after plants were installed. Primary themes were accessibility of supply, affordability, and hospital readiness. Respondents indicated a substantial increase in their hospital's ability to access lower-cost oxygen, with many attributing this to the locality of plants and reduced transportation barriers. However, other challenges persisted, and the emergence of COVID-19 1 year after plant installation and a civil conflict exacerbated supply shortages. Investments in equipment, supplies, and training optimized clinical utilization of oxygen and were highlighted as a need for ongoing investment. CONCLUSION: To achieve maximum impact, investments in large-scale oxygen systems must be accompanied by strategic plans to transport oxygen, reduce costs to hospitals, and provide support to clinical teams through equipment, supply procurement, and clinical training. These findings support comprehensive ecosystem approaches to strengthening oxygen access for sustainable impact.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".