Challenges of scaling up and of knowledge transfer in an action research project in Burkina Faso to exempt the worst-off from health care user fees
Bibliographic record
Abstract
BACKGROUND: Systems to exempt the indigent from user fees have been put in place to prevent the worst-off from being excluded from health care services for lack of funds. Yet the implementation of these mechanisms is as rare as the operational research on this topic. This article analyzes an action research project aimed at finding an appropriate solution to make health care accessible to the indigent in a rural district of Burkina Faso. RESEARCH: This action research project was initiated in 2007 to study the feasibility and effectiveness of a community-based, participative and financially sustainable process for exempting the indigent from user fees. A interdisciplinary team of researchers from Burkina Faso and Canada was mobilized to document this action research project. RESULTS AND KNOWLEDGE SHARING: The action process was very well received. Indigent selection was effective and strengthened local solidarity, but coverage was reduced by the lack of local financial resources. Furthermore, the indigent have many other needs that cannot be addressed by exemption from user fees. Several knowledge transfer strategies were implemented to share research findings with residents and with local and national decision-makers. PARTNERSHIP ACHIEVEMENTS AND DIFFICULTIES: Using a mixed and interdisciplinary research approach was critical to grasping the complexity of this community-based process. The adoption of the process and the partnership with local decision-makers were very effective. Therefore, at the instigation of an NGO, four other districts in Burkina Faso and Niger reproduced this experiment. However, national decision-makers showed no interest in this action and still seem unconcerned about finding solutions that promote access to health care for the indigent. LESSONS LEARNED: The lessons learned with regard to knowledge transfer and partnerships between researchers and associated decision-makers are: i) involve potential users of the research results from the research planning stage; ii) establish an ongoing partnership between researchers and users; iii) ensure that users can participate in certain research activities; iv) use a variety of strategies to disseminate results; and v) involve users in dissemination activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.263 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".