Investigação dos óbitos infantis através do modelo Calgary de avaliação e intervenção em famílias
Bibliographic record
Abstract
Objetivo : relatar la aplicacion del Modelo Calgary de Evaluacion e Intervencion en Familia (MCEIF) en la entrevista con los familiares durante la investigacion de los obitos infantiles en dos municipios del extremo sur del estado de Rio Grande do Sul. Metodos : estudio cualitativo, descriptivo y exploratorio realizado en los anos de 2005 a 2007, en dos municipios distintos del extremo sur del estado de Rio Grande do Sul a traves de visitas domiciliarias . La colecta de datos ocurrio en 2007. Para realizar el analisis de los datos se considero la informacion obtenida durante la entrevista, y la construccion del genograma y ecomapa e las colectadas em lo formulario de investigacion obito infantil. El estudio fue aprobado por el Comite de Etica en Investigacion de la Faculdad de Medicina de la Universidad Federal de Pelotas (0 63/07 ). Resultados : el Modelo Calgary de Evaluacion e Intervencion en Familia fue util al abordar a las familias enlutadas con la perdida precoz de uno de sus miembros, aproximando no solo el profesional enfermero a la familia como fortaleciendo la relacion de la familia con el sistema publico de salud. Conclusion : este estudio permitio comprender que el enfermero, a traves de la realizacion del cuidado a la familia bajo el enfoque sistemico posibilito su acogida, proporcionando una asistencia ampliada para las necesidades de la familia. Descriptores : mortalidad infantil; enfermeria; familia; salud publica.
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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 | 0.016 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".