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Record W4400975188 · doi:10.3390/healthcare12151476

Unmasking Elder Abuse: Depression and Dependency in the Post-Pandemic Era

2024· article· en· W4400975188 on OpenAlexaboutno aff
Isabel Iborra-Marmolejo, Cristina Aded-Aniceto, Carmen Moret‐Tatay, Gloria Bernabé Valero, María José Jorques-Infante, María José Beneyto-Arrojo

Bibliographic record

VenueHealthcare · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
FundersUniversidad de Oviedo
KeywordsPandemicDepression (economics)Dependency (UML)Elder abuseCoronavirus disease 2019 (COVID-19)PsychiatryPsychologyMedicineGerontologySuicide preventionMedical emergencyPoison controlEngineeringInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study was to analyze elder abuse in people over 65 years of age and its relationship with some risk factors-depression symptoms, dependency, gender and age-in the Spanish population. METHODS: = 6.46), including the Abbreviated Yesavage Scale to assess depression, the Katz Index for Basic Activities of Daily Living to assess dependency, and the American Medical Association and the Canadian Task Force Questionnaire to assess suspicion of abuse. RESULTS: A prevalence of 40.72% of suspected abuse, of 5.99% of established depression, and of 1.20% of severe dependence was obtained. The prevalence of abuse was higher in the population with dependency (75%) than without dependency (37%). In the case of depression, the prevalence of abuse was 70% for people with established depression and 35.4% for people without depression. CONCLUSION: Women have higher rates of abuse than men, although this difference is not statistically significant. The same occurs with age. Nevertheless, having established depression and dependency are confirmed risk factors for suffering abuse.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.355
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.367
Teacher spread0.337 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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