Maximizing the Value of Donated Medical Equipment in Resource-limited Settings: The Roles of Donors and End-users
Bibliographic record
Abstract
Background: Medical technologies, such as medical devices, are essential to healthcare delivery yet, in some settings there is a pervasive shortage that severely limits the capacity of clinicians to assess, diagnose, treat, monitor, and prevent diseases. Transnational funders have taken on the responsibility of providing funds for almost 80% of medical equipment in countries with severe shortages, yet 70% of equipment goes unused and abandoned. This means that, in the best-case scenario, for every dollar spent on donations, at least 62.5 cents goes to waste. The question then arises about why funders continue to support such magnitudes of waste in settings where demand for improving healthcare is so high.Objective: This study aims to understand the roles of transnational donors and end-users in defining and implementing equipment donation and funding policies in Accra, Ghana. The study will explore how existing policies on equipment donation incorporate transparency and accountability. The study will also identify opportunities for optimizing the value of donations and minimizing waste for the end-users.Methodology: We will develop a case study based on a review of policy and guideline documents, and on survey data and interviews of technicians, nurses, physicians, and staff of funding organizations. Potential Impact: This study will contribute to the growing body of research at the intersection of health policy, transnational philanthropy, and development. Findings from this study will be useful to policymakers in improving utility (or value) of medical equipment, and subsequently enhancing health benefits.
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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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".