INVESTIGATING THE CONNECTION BETWEEN AGEISM AND ELDER ABUSE
Bibliographic record
Abstract
Abstract Elder abuse is recognized as a pervasive public health problem with detrimental consequences for older adults and society. Although considerable research has examined elder abuse risk factors at the individual level, there is a growing call for the field to move beyond proximal causes and consider broader socio-cultural and structural factors that influence elder abuse. Illustrating this shift, organizations, advocacy groups and researchers have proposed a connection between ageism and elder abuse. However, despite the assertion that ageism is a causal factor for elder abuse, there is a scarcity of research to demonstrate this relationship, and a coherent theoretical framework linking ageism to elder abuse remains to be articulated. The purpose of the current study was to examine the conceptual pathways and limited empirical research connecting ageism and elder abuse, and to develop a conceptual model that links ageism and elder abuse. We conducted a comprehensive review and synthesis of the ageism/elder abuse literature, as well as research from other domains of interpersonal/family violence. Based on this synthesis, the proposed model includes plausible mediators (social isolation, devaluation, depersonalization, infantilization, powerlessness, blame) and moderators (intersection with socio-cultural identities, internalized ageism, policy/social norms) that could be targeted as mechanisms of change in interventions designed to address the issue. As such, it provides a framework for hypothesis-testing and future research on the topic. This study informs a research agenda to bring conceptual clarity and empirical evidence to the study of the connection between ageism and elder abuse.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".