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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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)0.021
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0080.002
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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