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Record W6887833940 · doi:10.17632/3kt8gyckfb.1

Risk of violence in elderly people in Brazil: representativeness of the age group // Risco de violência em pessoas idosas no Brasil: representatividade da faixa etária

2024· dataset· en· W6887833940 on OpenAlexaboutno aff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2024
Typedataset
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentativeness heuristicDepression (economics)Public healthElderly peopleScale (ratio)OddsGeriatric Depression ScaleAge groups

Abstract

fetched live from OpenAlex

This study aimed to identify the association and risk factors according to the age group of the elderly. This is an observational, cross-sectional study with a quantitative approach carried out with 200 elderly people assisted by Primary Care in Foz do Iguaçu, PR. They were surveyed using instruments for sociodemographic and health characterization, functionality (Lawton and Brody Scale for Instrumental Activities of Daily Living - IADL), frailty (Edmonton Frail Scale - EFE), depression (Geriatric Depression Scale- GDS-15), and risk of violence (Sclate Hwalek-Sengstock Elder Abuse Screening Test - H-S/EAST). The data were analyzed using Excel software version 2010 and the Statistical Package for the Social Sciences. Chi-square and Odds Ratio with 95% CI were used, with a p-value < 0.05. For the 60-70 age group and the over-70 age group, the risk of violence was associated with schooling, functionality, depression, and frailty. The risk of violence was also associated with race for those aged over 70. All the results were significant for the group with no risk of violence. Violence against the elderly is a public health problem, and health managers need to work together to implement new public policy strategies aimed at promoting and protecting this segment of the population.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.324
Teacher spread0.307 · 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 teacher head, not a consensus.

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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