Defining an action-research’s content to improve a policy supporting indigents’ health in Mali: a concept mapping
Bibliographic record
Abstract
Background Concept mapping (CM) is a method used to create consensus around a concept within a group of actors, which enables an empowering process for the participants through dialogues and shared information. We performed a CM, aiming to improve the operationalisation of a health policy, the RAMED, to promote healthcare access to the indigent population in Mali. Methods The CM followed five steps leading to the conceptual map development: 1) brainstorming, 2) statements’ scoring, 3) clustering, 4) statistical (hierarchical clustering and multi-dimensional scaling) and qualitative analysis, and 5) validating the map. Twenty-seven participants took place in the CM, representing eighteen organisations linked to the implementation of the policy. Results We identified seven clusters of activities towards finding the concrete and collective solutions to improve healthcare access: “funding strengthening,” “integral management and care of indigents,” “expertise creation,” “promotion and communication,” “indigents’ identification processes,” “monitoring and evaluation,” and “integration and coordination of actors.” According to scalability and priority scores, “identification processes” was the most scalable and important cluster (3.03 [±0.51] and 3.26 [±0.47]/4 respectively), while “funding strengthening” was the least scalable and important (2.59 [±0.47] and 2.76 [±0.42]/4 respectively). Conclusions Although this method is primarily exploratory and a great starting point for further collaborative research, it managed to highlight the two fundamental issues in action-research: the difficulty related to the knowledge transfer to vulnerable populations and their lack of participation in the research process. It is particularly an issue in West Africa, due to lack of empirical studies and high poverty levels. Results of this study demonstrate that CM offers an important starting point for improvements, which should focus on knowledge transfer and inclusion of vulnerable populations’ points of view.
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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 | 0.121 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".