Bibliographic record
Abstract
BACKGROUND: This paper identifies core elements in principal definitions of elder abuse or mistreatment of older adults (EA/MOA) and discusses the relevance of four crucial concepts: age, vulnerability, trust, and power balance in relationships. METHOD: A critical analysis of selected literature in EA/MOA with a focus on works from the last 10 years. RESULTS: Current definitions of EA/MOA share commonalities regarding an understanding of elder abuse as a status offence, the inclusion of both acts and omissions, and the consideration of multiple levels of behavior and its effects. Definitions differ with regard to aspects as crucial as the intentionality of an abusive action and its actual or potential harmful effects. EA/MOA can be considered as a complex subtype of victimization in later life limited to victim-perpetrator relationships, where the perpetrator has assumed responsibility for the victim, the victim puts trust in the offender, or the role assigned to the offending person creates the perception and expectation that the victim may trust the perpetrator. Vulnerability is identified as a key variable in EA/MOA theory and research. With regard to neglect, the mere possibility of being neglected presupposes a heightened level of vulnerability. Power imbalance often characterizes victim - perpetrator relationships but is not a necessary characteristic of abuse. CONCLUSION: Research on EA/MOA needs conceptual development. Confining phenomena of EA/MOA to specific relationships and tying them to notions of vulnerability has implications for research design and sampling and points to the limits of population-based victimization surveys.
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 | 0.068 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".