Contribution des diplômés du master en santé internationale et politiques nutritionnelles au développement en Afrique
Bibliographic record
Abstract
OBJECTIVES: This study aims to analyze how graduates, coming from low income Francophone countries, of the Master in International Health and Nutrition Policies program at the Senghor University are a new human capital bringing an original contribution to the global development of African communities. METHODS: A secondary analysis of qualitative findings responded to the research question about the evidences of the impact of this new human capital over the community development of countries where the graduates worked. Findings also revealed graduates' potential to contribute to future endeavors in global health. The analysis was conceptually framed by the population health promotion model and the dyad of human and social capital. Findings were analyzed applying the thematic analysis method focusing on four themes: profile of human capital, impacts and results, review of contextual conditions and expected results. RESULTS: Accounts of 70 graduates indicated an emergence of a new profile of human capital due the reinforcement of their qualities, valorization of their assets and consolidation of their skills. As multi-level knowledge brokers, the graduates brought an original contribution to the African development by enhancing individuals' competencies. CONCLUSIONS: Graduates contributed to all social actors by socializing knowledge they acquired, as well as by integrating themselves in social relations at all levels resulting in the mobilization and promotion of individuals' competencies. Thus, graduates reinforced the positive actions of the existing human capital.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".