Equity and health policy in Africa: Using concept mapping in Moore (Burkina Faso)
Bibliographic record
Abstract
BACKGROUND: This methodological article is based on a health policy research project conducted in Burkina Faso (West Africa). Concept mapping (CM) was used as a research method to understand the local views of equity among stakeholders, who were concerned by the health policy under consideration. While this technique has been used in North America and elsewhere, to our knowledge it has not yet been applied in Africa in any vernacular language. Its application raises many issues and certain methodological limitations. Our objective in this article is to present its use in this particular context, and to share a number of methodological observations on the subject. METHODS: Two CMs were done among two different groups of local stakeholders following four steps: generating ideas, structuring the ideas, computing maps using multidimensional scaling and cluster analysis methods, and interpreting maps. Fifteen nurses were invited to take part in the study, all of whom had undergone training on health policies. Of these, nine nurses (60%) ultimately attended the two-day meeting, conducted in French. Of 45 members of village health committees who attended training on health policies, only eight were literate in the local language (Moore). Seven of these (88%) came to the meeting. RESULTS: The local perception of equity seems close to the egalitarian model. The actors are not ready to compromise social stability and peace for the benefit of the worst-off. The discussion on the methodological limitations of CM raises the limitations of asking a single question in Moore and the challenge of translating a concept as complex as equity. While the translation of equity into Moore undoubtedly oriented the discussions toward social relations, we believe that, in the context of this study, the open-ended question concerning social justice has a threefold relevance. At the same time, those limitations were transformed into strengths. We understand that it was essential to resort to the focus group approach to explore deeply a complex subject such as equity, which became, after the two CMs, one of the important topics of the research. CONCLUSION: Using this technique in a new context was not the easiest thing to do. Nevertheless, contrary to what local organizers thought when we explained to them this "crazy" idea of applying the technique in Moore with peasants, we believe we have shown that it was feasible, even with persons not literate in French.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".