Barriers to scaling health technologies in sub-Saharan Africa: Lessons from Ethiopia, Nigeria, and Rwanda
Bibliographic record
Abstract
Given the importance of effectively scaling technologies in the promotion of health and innovation, the objective of our paper was to identify the barriers that can impede the process of scaling up in sub-Saharan Africa. We reviewed the published literature and collected data through semi-structured interviews of six key players in the health technology space in three sub-Saharan African countries: Ethiopia, Nigeria, and Rwanda. We analyzed the interview transcripts in light of the transition theory framework. This informative framework highlights how health technologies are nested in societal contexts, how they could be scaled up, and what factors influence changes in structure, practice, and culture. Our data analyses uncovered six barriers to scaling health technologies. These barriers included inadequate availability and accessibility of health equipment, inadequate policies and gaps in policy effectiveness, lack of financial resources, unavailability of health personnel and expertise, insufficient understanding and infrastructure to scale up effectively, and lack of community and user integration with the technology. Through the results of this study, we have created a narrative on how health technologies can be most effectively scaled in sub-Saharan Africa and provided insights for researchers, policymakers, and programme implementers on the scaling of health technologies in the region.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".