Enhancing sustainability of medical devices procurement in Low- and Middle-Income Countries
Bibliographic record
Abstract
this research originates from the observation that a significant proportion of Medical Devices (MDs) in Low- and Middle-Income Countries (LMICs) remain unused. Unused MDs in the public health sector are the result of an unsustainable procurement that does not consider the existence or creation of the conditions for a safe, effective and sustainable use of the MD. Focusing on the causal factors behind unused MDs, this study aims to explore how procurement processes can be improved to avoid this unsustainable waste of resources. A systems thinking approach was applied to investigate the root causes of the failure of the processes involved in MD procurement. Beginning with the development of a diagram based on a literature analysis and expert panel judgements, this research resulted in the recommendation of three key leverage points to be implemented during the procurement of MDs: conducting robust, evidence-based assessments of local needs, conditions, capabilities and constraints; involving a multidisciplinary team of experts in the procurement process; and strengthening local clinical engineering capabilities. The results show how sustainable procurement shall primarily focus on effective, long-term use of MDs, strengthening procurement governance and resources appropriate use, and assess their environmental, social, and financial impacts as second steps.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".