Beyond reverse innovation in healthcare: A step towards global health justice through reciprocity
Bibliographic record
Abstract
Reverse innovation is the flow of ideas from lower to higher income countries. This has received growing attention in healthcare research for its potential to provide cost-effective solutions to pervasive health inequities, human resource shortages and rising health expenditures. Even though the underlying premise has its merits, the use of the term itself has become controversial as some argue it implies that innovation normally flows in the other direction. In this commentary, we first discuss some of the criticisms voiced against the term. With these in mind, we subsequently make the case for an alternative approach and describe how we work to implement this in our own research project. More specifically, we suggest a move towards reciprocal innovation as a more equitable, mutually beneficial form of learning and knowledge sharing. We present the COMPASS (Community Health Workers for Primary Care Access) project which will provide an empirically grounded example of reciprocal innovation in practice. The aim of the COMPASS project is to adapt a community health worker intervention from Brazil and South Africa for implementation in Belgium. The project has the potential to provide valuable lessons for all parties involved.
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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 | 0.093 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.084 |
| Scholarly communication | 0.025 | 0.040 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".