Neglect, abuse and violence against older women: Definitions and research frameworks
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".