Enhancing regional capacity in chronic disease surveillance in the Americas
Bibliographic record
Abstract
Existe la necesidad de reforzar la capacidad regional para la vigilancia de las enfermedades cronicas en las Americas. Los objetivos de este articulo son 1) ofrecer nuestro apoyo decidido a favor de la vigilancia de las enfermedades cronicas, 2) presentar una revision descriptiva y un resumen de las actividades de vigilancia y los problemas en torno a las mismas en las Americas, 3) confeccionar una lista de recursos y fuentes de consulta para obtener mas informacion, y 4) ofrecer unas recomendaciones para reforzar la capacidad regional. Este articulo se basa en una revision personal de informes, sitios de Internet y apuntes personales procedentes de diversos proyectos, reuniones y actividades relacionados con la vigilancia de las enfermedades cronicas en las Americas, y en un analisis a profundidad de los materiales recopilados. Se ha determinado que las agencias sanitarias internacionales, los gobiernos de diversos paises, las organizaciones no gubernamentales y los profesionales de la sanidad publica han dedicado grandes esfuerzos a la construccion y al desarrollo de las capacidades de vigilancia de las enfermedades cronicas en la Region. Para seguir apoyando el aumento de dichas capacidades, se hace necesario establecer una red de redes (una metarred) cuya mision deberia ser la vigilancia de la vigilancia. Siete aspectos importantes para el aumento de esta capacidad son la estrategia, la colaboracion, la informacion, la educacion, la novedad, la comunicacion, y la evaluacion.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".