Bibliographic record
Abstract
Introducción: Los gobiernos de todo el mundo buscan diferentes estrategias para ampliar el acceso a los servicios de salud, abaratar costos y optimizar resultados. Objetivo: Presentar el contexto socio-político de salud en el que se sitúan las guías de buenas prácticas basadas en la evidencia de la Asociación de Enfermeras de Ontario. Temas de reflexión: La discusión se centra en los desafíos claves de salud y atención sanitaria que enfrentan diversos países del mundo, y las amenazas y oportunidades que éstos presentan para Enfermería. Se enfatiza la necesidad del uso de evidencia como una herramienta central pero no la única para optimizar los resultados de salud de los ciudadanos. Conclusiones: Los cuatro componentes principales que facilitan un modelo de atención efectivo corresponde a: Recursos humanos adecuados; trabajo interprofesional positivo; expansión del rol de las enfermeras profesionales; y la práctica basada en la evidencia. Todos estos componentes son necesarios para alcanzar una atención de acceso universal, oportuna y centrada en la persona. [Grinspun D. Modelo de Enfermería para optimizar sistemas de salud. MedUNAB 2017; 20(2): 224-234].
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".