The use of sustainable and scalable health care technologies in developing countries
Bibliographic record
Abstract
The use of sustainable and scalable health care technologies in developing countries Gari D Clifford1–3 1Department of Biomedical Informatics, Emory University, Atlanta, GA, USA; 2Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA; 3Centre for Affordable Health Care Technology, Kellogg College, University of Oxford, Oxford, UK Abstract: Although health care technology is progressing, in important ways access is dropping for many communities across the globe. Contributing factors include increasing awareness and options for diagnosis and treatment, and hence expectations, with consequential increases in complexity, training, and costs for health service delivery. Changes in wealth distribution, aging populations, and increasing burdens from chronic conditions also exacerbate these issues. Many attempts at delivering affordable health care technologies into resource-poor regions of the world have had limited impact, often with a focus only on cost or simplicity. However, with close attention to appropriate design considerations, including sustainable business practices and local cultural dynamics, there is the potential to revolutionize the field and deliver health care support technologies that empower, educate, and learn from the user. This article discusses the main concepts in designing relevant and sustainable health care technologies for resource-constrained communities and proposes structural ecosystem changes that may be required to achieve this goal. Keywords: affordable health care, open source, scalable technology, developing countries, emerging economies
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".