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Record W3134849782 · doi:10.5205/110

Investigação dos óbitos infantis através do modelo Calgary de avaliação e intervenção em famílias

2009· article· pt· W3134849782 on OpenAlexaboutno aff
Sidnéia Tessmer Casarin, Teila Ceolin, Rita de Cássia Mourão dos Reis Carvalho, Rita Maria Heck, Eda Schwartz, José Richard de Sosa Silva

Bibliographic record

VenueJournal of Nursing Ufpe Online · 2009
Typearticle
Languagept
FieldHealth Professions
TopicMaternal and Neonatal Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.127
GPT teacher head0.467
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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