Les barrières à l'appropriation communautaire d'un projet de prévention et de prise en charge de qualité de la malnutrition aigüe au Niger
Bibliographic record
Abstract
Sigles et abréviations CogES -Comité de gestion CoSan -Comité de Santé CrEnaM -Centre de Récupération et d'Éducation Nutritionnelle Ambulatoire pour la Malnutrition Modérée CrEnaS -Centre de Récupération et d'Éducation Nutritionnelle Ambulatoire pour la Malnutrition Sévère CrEnI -Centre de Récupération et d'Éducation Nutritionnelle Intensives (soins prodigués aux patients hospitalisés pour les cas compliqués) CSI -Centre de Santé Intégré DS -District Sanitaire hELP -horizon d'Échange et de Lutte contre la pauvreté (citée BA) hD -hôpital de District IBW -Institutions de Bretton Woods MaS -Malnutrition Aigüe Sévère nIg -nom des programmes de l'ONG HELP au Niger (citéM1pour NIG46 et M2 pour NIG47) oCDE -Organisation de Coopération et Développement Économiques oDD -Objectifs de Développement Durable oMS -Organisation Mondiale de la Santé ong -Organisation Non gouvernementale PaM -Programme Alimentaire Mondial PnSn -Plan National de Sécurité Nutritionnelle PDS -Plan de Développement Sanitaire VIh -Virus de l'Immunodéficience Humaine Dans le présent document, le masculin est utilisé dans le seul but d'alléger le texte et d'en faciliter la lecture.
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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 | 0.007 | 0.021 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".