Evaluation of a knowledge transfer strategy from a user fee exemption program for vulnerable populations in Burkina Faso
Bibliographic record
Abstract
As part of this special issue contributing to the development of knowledge on vulnerability and health in Africa, this article analyzes one example of a knowledge transfer strategy aimed at improving the use of research results that could help reduce the vulnerability of certain populations. In this case, since September 2008, the Non-Governmental Organization (NGO) Hilfe zur Selbsthilfe e.V. (HELP) has conducted a trial of subsidizing 100% of the costs of health care for vulnerable populations in two health districts of Burkina Faso. A scientific partnership was created to produce evidence on the intervention, and a knowledge transfer strategy was developed to promote the use of that evidence by stakeholders (decision-makers, people working in the health system, funding partners, etc.). The results showed that considerable efforts were invested in knowledge transfer activities and that these led to all types of use (instrumental, conceptual, persuasive). However, considerable variation in use was observed from one setting to another. This article presents some lessons to be drawn from this experience.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".