Social and environmental risk factors for trachoma: a mixed methods approach in the Kembata Zone of southern Ethiopia
Bibliographic record
Abstract
The influence of 14 predictor variables on active trachoma risk and disease severity in children was investigated using mixed effects logistic regression. Young age, unclean face and low household expenses were risk factors for active disease. Older age and unclean face were risk factors for Trachomatous inflammation, intense (TI), the more severe form of active disease. Interviews and focus groups revealed that lack of food, water and money were of greatest concern to the communities surveyed. The results of the qualitative and quantitative analyses converged, supporting continued implementation of the facial cleanliness (‘F’) and environmental improvement (‘E’) components of the WHO's SAFE strategy. Résumé A l'aide de régression logistique à effets mixtes, ce papier analyse l'importance de quatorze facteurs prédictifs liés aux risques du « trachome actif » chez les enfants. Le jeune âge, le défaut d'hygiène faciale et le faible niveau de dépenses des ménages ont été les facteurs à risque du « trachome actif ». Dans le cas de l'inflammation Trachomateuse-Intense (TI), la forme la plus sévère de la maladie, l'âge avancé et le défaut d'hygiène faciale ont été les principaux facteurs aggravant du trachome cécitant. Des entrevues et discussions de groupes, il en est ressorti que les défauts d'alimentation, d'eau et de revenu ont été les aspects les plus préoccupants pour les communautés étudiées. Les résultats des analyses qualitatives et quantitatives aboutissent à des conclusions similaires. Ils plaident pour une mise en œuvre continue du nettoyage du visage (N) et le changement de l'environnement (CE), deux interventions de la stratégie « CHANCE » développée par l'Organisme mondiale de la Santé.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".