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The 15-minute family interview: a family health strategy tool

2013· article· en· W1990138044 on OpenAlexaffabout
Mariana Cristina Lobato dos Santos Ribeiro Silva, Nancy J. Moules, Lucía Silva, Regina Szylit Bousso

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of Calgary
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsFamily healthNursingFamily medicinePsychologyGerontologyMedicine

Abstract

fetched live from OpenAlex

The 15-minute family interview is a condensed form of the Calgary Family Assessment and Intervention Models (CFAM and CFIM) that aims to contribute to the establishment of a therapeutic relationship between nurses and family and to implement interventions to promote health and suffering relief, even during brief interactions. This study investigated the experience of nurses from the Family Health Strategy (FHS) who used the 15-minute interview on postpartum home. The qualitative research was conducted in three stages: participants' training program, utilization of the 15-minute family interview by participants, and interviews with nurses. The data were collected through semistructured interviews with eight nurses. The thematic analysis revealed two main themes: dealing with the challenge of a new practice and evaluating the assignment. This work shows that this tool can be used to deepen relationships between nurses and families in the Family Health Strategy.

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)0.012
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.075
GPT teacher head0.347
Teacher spread0.272 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations9
Published2013
Admission routes2
Has abstractyes

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