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Record W1857908373 · doi:10.56801/seejph.vi.20

Neglect, abuse and violence against older women: Definitions and research frameworks

2023· article· en· W1857908373 on OpenAlexaboutno aff
Patricia Brownell

Bibliographic record

VenueSouth Eastern European Journal of Public Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsNeglectElder abuseDomestic violencePoison controlPopulationPsychologyMedicineLife course approachSuicide preventionGerontologyPsychiatryDevelopmental psychologyMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

The aging of the global population with women living longer than men, resulting in the feminization of aging, focuses attention on the intersection of gender and age. Women across the lifespan can be victims of violence but there has been little attention to date to the neglect, abuse and violence against older women. Because of this gap in knowledge and remedies, little is known about neglect, abuse and violence against older women, particularly its prevalence as well as evidence-based prevention and intervention strategies. Several definitions of neglect, abuse and violence are reviewed here, along with conceptual frameworks that operationalize these definitions differently, resulting in differences in findings on prevalence as well as fragmentation in the way that older women victims of abuse are viewed. Three definitions of older adult abuse are discussed, including those formulated by the Toronto Declaration, the National Research Council, and the United States Center forDisease Control. Each focuses on a different aspect of abuse of older women: active ageing, old age dependency, and domestic violence in later life. A fourth conceptual framework, the human rights perspective, shows promise for addressing abuse of older women in a more holistic manner than the other definitions, but Is not fully developed as a way of understanding neglect, abuse and violence against older women. This is the first of a four-part series on older women and 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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
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 integrity0.0000.001
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 head0.172
GPT teacher head0.377
Teacher spread0.205 · 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 designQualitative
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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueSouth Eastern European Journal of Public HealthSame topicElder Abuse and NeglectFrench-language works237,207