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Record W4399909112 · doi:10.1590/ce.v29i0.93836

EVALUACIÓN FUNCIONAL DE FAMILIAS DE PERSONAS CON CONDUCTA SUICIDA: APLICACIÓN DEL MODELO DE CALGARY

2024· article· es· W4399909112 on OpenAlexaboutno aff
Isabela Carolyne Sena de Andrade, Nadirlene Pereira Gomes, Cíntia Mesquita Correia, Ionara da Rocha Virgens, Josinete Gonçalves dos Santos Lírio, Joana D’arc Ferreira Lopes Santos, Sabrina de Oliveira Silva Telles

Bibliographic record

VenueCogitare Enfermagem · 2024
Typearticle
Languagees
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

RESUMEN: Objetivo: revelar las acciones que mantienen la funcionalidad de la familia de personas con conducta suicida. Método: se trata de un estudio cualitativo, descriptivo-exploratorio, basado en el marco teórico y metodológico del Modelo de Evaluación Familiar de Calgary. Once usuarios del Centro de Estudio y Prevención del Suicidio, ubicado en Salvador, Bahía, Brasil, participaron del encuentro online en 2022 para elaborar el genograma familiar. En la segunda etapa, la entrevista incluyó a nueve familias de usuarios. Resultados: surgieron las categorías: control por parte de los familiares; uso de la tecnología por parte de los familiares para mantener contacto diario; manejo de la crisis suicida por parte de los familiares y atención de los familiares a las necesidades básicas, todas las categorías se relacionan con la persona con conducta suicida. Conclusión: existen formas de implementar políticas y manuales de salud que orienten a los familiares y amigos para manejar la crisis suicida, y evitar consecuencias no deseadas, como el intento de suicidio y el acto consumado.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.062
GPT teacher head0.358
Teacher spread0.296 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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