Implementation of solar powered oxygen delivery in a conflict zone: preliminary findings from Somalia on feasibility and usefulness
Bibliographic record
Abstract
Access to therapeutic oxygen in low-resource settings remains a significant global problem. Solar powered oxygen (SPO2) delivery is a reliable and cost-effective solution. We followed implementation research methodology to gather data on engineering parameters (remote monitoring), nurse training (before and after knowledge questionnaire), patients treated with SPO2 (descriptive case series), and qualitative user feedback (focus group discussions). In January 2021, SPO2 was installed at Hanano General Hospital in Dusamareb, Galmudug State, Somalia, in a conflict-affected region. Daily photovoltaic cell output (median 8.0 kWh, interquartile range (IQR) 2.6-14) exceeded the electrical load from up to three oxygen concentrators (median 5.0 kWh, IQR 0.90-12). Over the first six months after implementation, 114 patients (age 1 day to 89 years, 54% female) were treated for hypoxaemic illnesses, including COVID-19, pneumonia, neonatal asphyxia, asthma, and trauma. Qualitative end user feedback highlighted SPO2 acceptability. Violent conflict was identified as a contextual factor affecting local oxygen needs. We provide the preliminary findings of this implementation research study and describe the feasibility, fidelity, rapid adoption, usefulness, and acceptability of SPO2 in a low-resource setting characterized by violent conflict during the COVID-19 pandemic. Our findings demonstrated the lifesaving feasibility of SPO2 in volatile 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".