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Family dynamics and social network of families of children with special needs for complex/continuous cares

2020· article· en· W3030171220 on OpenAlexaboutno aff
Beatriz Caroline Dias, Sônia Silva Marcon, Pamela dos Reis, Iven Giovanna Trindade Lino, Aline Cristiane Cavicchioli Okido, Sueli Mutsumi Tsukuda Ichisato, Eliane Tatsch Neves

Bibliographic record

VenueRevista gaúcha de enfermagem · 2020
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsDynamics (music)Psychological interventionPsychologyOrder (exchange)Developmental psychologyDescriptive researchApplied psychologySociologyPedagogyBusinessPsychiatrySocial science

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the family dynamics and the social support network for families of children with special needs of multiple, complex and continuous care. METHODS: A descriptive study of a qualitative approach, carried out in Maringá - PR, having as theoretical and methodological reference the Calgary Model of Family Assessment (CMFA). Data was collected through semi-structured audio-video interviews, carried out in the homes, together with 11 family caregivers of 13 children. RESULTS: Data is presented in the following categories: structural, developmental and functional evaluation, which show the changes in the family routine and the needs for the adjustment of the roles of its members, in order to better implement the care at home. CONCLUSIONS: Using the CMFA made it possible to identify and understand the composition, fragilities and potentialities of the family, as well as the relationships among its members and rearrangements to better enable care at home. This information favors interventions congruent with the needs of these families.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.057
GPT teacher head0.332
Teacher spread0.276 · 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

Citations36
Published2020
Admission routes1
Has abstractyes

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