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Record W4323925736 · doi:10.5281/zenodo.3228278

A STUDY ON ABUSE AND NEGLECT OF ELDERLY WOMEN LIVING IN FAMILIES

2019· article· en· W4323925736 on OpenAlexaboutno aff
T.R. Thirumalesha Babu, Sandeep Kadirudyavar

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsNeglectPsychologyDevelopmental psychologyGerontologyPsychiatryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Mistreatment of older people – referred to as ‘‘elder abuse’’ – was first described in British scientific journals in 1975 under the term ‘‘granny battering’’. As a social and political issue, though, it was the United States Congress that first seized on the problem, followed later by researchers and practitioners. During the 1980s scientific research and government actions were reported from Australia, Canada, China (Hong Kong SAR), Norway, Sweden and the United States, and in the following decade from Argentina, Brazil, Chile, India, Israel, Japan, South Africa, the United Kingdom and other European countries. Although elder abuse was first identified in developed countries, where most of the existing research has been conducted, anecdotal evidence and other reports from some developing countries have shown that it is a universal phenomenon. That elder abuse is being taken far more seriously now reflects the growing worldwide concern about human rights and gender equality, as well as about domestic violence and population ageing.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.357
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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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