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Frailty and sociodemographic and health factors, and social support network in the brazilian elderly: A longitudinal study

2021· article· en· W4200391773 on OpenAlexaboutno aff
Jack Roberto Silva Fhon, Luí­pa Michele Silva, Suellen Borelli Lima Giacomini, Nayara Araújo dos Reis, Marcela Cristina Resende, Rosalina Aparecida Partezani Rodrigues

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGerontologySocial supportLongitudinal studySocial network (sociolinguistics)PsychologyMedicineDemographySociologyComputer scienceSocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify and analyze the sociodemographic and health factors and the social support network of the elderly associated with frailty in the assessments carried out between 2007/2008 and 2018. METHODS: This is a longitudinal study with elderly people aged ≥65 years living in the community. The instruments used were those for Demographic Profile, the Mini Mental State Examination, the Functional Independence Measure, Lawton and Brody Scale, Geriatric Depression Scale, Minimum Relationship Map for the Elderly, and Edmonton Frail Scale. Descriptive analysis and linear regression were used, all tests with p < 0.05. RESULTS: Of the 189 elderly in the study period (2007/2008-2018), most were 80 years old and over, with an average of 82.31 years old; they were women, with no partner, who lived with other family members and were retired. In the final analysis, regardless of age and sex, a decrease in functional independence, an increase in depressive symptoms, an increase in the number of self-reported illnesses, and an increase in the frailty score were observed. CONCLUSION: The factors that were associated with the increase in frailty of the elderly during the study period were age, female sex, and no partner. The health team, which includes nurses, shall be aware of changes and develop care plans to prevent or avoid their progression.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.095
GPT teacher head0.373
Teacher spread0.278 · 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

Labeled directly by 2 models reading the full record.

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

Citations14
Published2021
Admission routes1
Has abstractyes

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Same venueRevista da Escola de Enfermagem da USPSame topicFrailty in Older AdultsFrench-language works237,207