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Fragilidade e apoio social e familiar de idosos em contextos de vulnerabilidade

2018· article· pt· W2901978165 on OpenAlexaboutno aff
Isabela Thaís Machado de Jesus, Ariene Angelini dos Santos‐Orlandi, Marisa Silvana Zazzetta

Bibliographic record

VenueRev Rene · 2018
Typearticle
Languagept
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsVulnerability (computing)Social vulnerabilityGerontologyContext (archaeology)Social supportSocial environmentElderly peopleScale (ratio)PsychologyMedicineSociologyGeographySocial psychologyPsychological resilienceComputer securityComputer science

Abstract

fetched live from OpenAlex

Objective: to analyze the relationship between of frailty and the family social relationships of the elderly in a context of social vulnerability. Methods: a cross-sectional study with elderly people enrolled in five Reference Centers for Social Assistance. Sample for convenience composed of 247 elderly. For data collection, a sociodemographic questionnaire, Edmonton Frailty Scale, Genogram and Eco-maps were used. Social vulnerability characterized according to Social Vulnerability Index. Results: of the respondents, 41.7% did not present frailty, 21.5% were apparently vulnerable and 36.8% frail. There was no significant difference between frailty and family relationship. There was significant difference between frailty and external attachment (p=0.010), indicating that elderly individuals with frailty at some level had a limited external link. Conclusion: elderly people who have a close relationship with family members, did not present frailty, while the majority of the elderly who do not have external ties, presented some level of frailty.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.030
GPT teacher head0.323
Teacher spread0.293 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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