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Elderly victims of violence: family assessment through the Calgary model

2022· article· en· W4205887713 on OpenAlexaboutno aff
Miriam Fernanda Sanches Alarcon, Bruna Carvalho Cardoso, Caroline Borges, Daniela Garcia Damaceno, Viviane Boacnin Yoneda Sponchiado, María José Sanches Marín

Bibliographic record

VenueRevista gaúcha de enfermagem · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSadnessAngerPsychologyGenogramFeelingUnit (ring theory)Developmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To understand the structure, development, and functionality of the family of the elderly victim of violence. METHOD: Descriptive research with a qualitative approach, based on the Calgary Family Assessment Model. Four elderly people who suffered violence and their family members were assessed at home, from October to November 2019. Data analysis was based on the genogram and ecomap, as proposed in the model. RESULTS: It was found that the members of the four families had low schooling and financial difficulties. As for the social support network, the neighbors, the health unit and the Church stood out. The members of each family nucleus expressed feelings of fear, insecurity, anger, nervousness, sadness and impotence, resulting from the conflict between the couple. FINAL CONSIDERATIONS: In the assessed families, weaknesses and potentialities in the structure, development and functioning are highlighted, which must be considered in the elaboration of the care plan.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
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 head0.046
GPT teacher head0.363
Teacher spread0.316 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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