Cost analysis and critical success factors of the use of oxygen concentrators versus cylinders in sub-divisional hospitals in Fiji
Bibliographic record
Abstract
BACKGROUND: Oxygen is vital in the treatment of illnesses in children and adults, yet is lacking in many low and middle-income countries health care settings. Oxygen concentrators (OCs) can increase access to oxygen, compared to conventional oxygen cylinders. We investigated the costs and critical success factors of OCs in three hospitals in Fiji, and extrapolated these to estimate the oxygen delivery cost to all Sub-Divisional hospitals (SDH) nationwide. METHODS: Data sources included key personnel interviews, and data from SDH records, Ministry of Health and Medical Services, and a non-governmental organisation. We used Investment Logic Mapping (ILM) to define key issues. An economic case was developed to identify the investment option that optimised value while incorporating critical success factors identified through ILM. A fit-for-purpose analysis was conducted using cost analysis of four short-listed options. Sensitivity analyses were performed by altering variables to show the best or worst case scenario. All costs are presented in Fijian dollars. RESULTS: Critical success factors identifed included oxygen availability, safety, ease of use, feasibility, and affordability. Compared to the status quo of having only oxygen cylinders, an option of having a minimum number of concentrators with cylinder backup would cost $434,032 (range: $327,940 to $506,920) over 5 years which would be 55% (range: 41 to 64%) of the status quo cost. CONCLUSION: Introducing OCs into all SDHs in Fiji would reduce overall costs, while ensuring identified critical success factors are maintained. This study provides evidence for the benefits of OCs in this and similar 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".