Linking Social Capital to Therapeutic Practices in Korhogo, Côte d’Ivoire
Bibliographic record
Abstract
In the northern areas of Cote d’Ivoire most affected by the 2010/2011 post-electoral armed conflict, the functioning and use of health services were negatively influenced. This study seeks to describe the nature of the social capital of Korhogo households and to analyze its influence on their choices in terms of therapeutic practices. The study, both quantitative and qualitative, was carried out in the North of the country, more precisely in Korhogo, a city located in the Poro region. The questionnaire was administered to 588 heads of households in 49 enumeration areas. In these EAs, 38 semi-structured interviews and 6 focus groups were also organized with health workers, traditional healers and local leaders. Overall, households perceive two forms of social capital: individual social capital and collective social capital. On the basis of this more or less clear knowledge of social capital, almost all households (92.8%) say they rely on the “human resources” dimensions compared to just over two-thirds (69.2%) for those relating to financial resources. Modern medicine (36.1%) and traditional medicine (32.8%) are the most dominant in the region. The majority of households (83.0%) are led to opt for a therapeutic practice following discussions with the members of their networks. However, the human, material and financial dimensions of social capital have little influence on the choice of therapeutic practices for households. All initiatives aimed at strengthening solidarity are likely to contribute to promoting the health and well-being of disadvantaged households in situations of socio-political crises.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".