Cervical-uterin cancer prevention analyzed through evaluative research: Multi-actoral assesment of policie’s scope in Gran San Juan
Bibliographic record
Abstract
El Programa Provincial de Prevención de Cáncer Cervicouterino busca disminuir la tasa de incidencia y mortalidad por esta enfermedad en mujeres cuyo acceso a la salud se ve condicionado por múltiples factores. El diseño de la política gira en torno a la salud de las mujeres y al Estado como garante de ese derecho.El estudio analiza las principales acciones y resultados del programa, obtenidos en el periodo 2014-2016, en el Gran San Juan, a través de la investigación evaluativa. Se adoptó un enfoque mixto de investigación a fin de recuperar datos cuantitativos y cualitativos que permitan conocer, describir e interpretar el impacto de la enfermedad, la valoración sobre la política de salud desde la visión de mujeres y profesionales y poder construir lecciones aprendidas y recomendaciones que refuercen la política.Los resultados obtenidos permiten visibilizar la problemática incorporándola en la agenda pública local. El programa se desarrolla en un sistema de salud desarticulado donde las acciones de prevención resultan aisladas. Dificultades de infraestructura, escasez de recurso humano, cultura patriarcal dominante, responsabilización de la mujer sobre el cuidado personal, sumado a la débil cultura evaluativa de las políticas públicas, son los principales factores que influyen sobre el logro de los objetivos del programa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".