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Entrevistando as famílias de recém-nascidos mal-formados como proposta de avaliação e de intervenção de enfermagem

2004· article· pt· W1834674007 on OpenAlexaboutno aff
Maria das Graças de Oliveira Fernandes, Dirce Laplaca Viana, Flávia Simphronio Balbino, Ana Lúcia de Moraes Horta

Bibliographic record

VenueActa Scientiarum Health Sciences · 2004
Typearticle
Languagept
FieldHealth Professions
TopicMaternal and Neonatal Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)Active listeningConversationPsychologySick childNursingDevelopmental psychologyHumanitiesMedicinePsychotherapistPediatricsCommunicationArt

Abstract

fetched live from OpenAlex

This paper is based on a study that searched ways to understand how to deal with the family who experienced the news of a sick newborn. The interview was performed in a hospital located in São Paulo, Brazil, in November of 2002 and it used the Calgary Model of Assessment and Intervention in Family (CMAF/CMIF)). This study showed the need for intervention with the family to minimize the impact of receiving the news of a sick newborn, and to provide support through a conversation for resolution of the problem. The function of the nurse is to try to perform a precocious work with the family, facing the difficult experience of having a special child. The intervention aims to help the family to solve the problem, listening to the verbal and not verbal communication, searching to understand the situation with the family.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0040.001
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.458
Teacher spread0.358 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2004
Admission routes1
Has abstractyes

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