Distressing Aspects of Elder Abuse Victimization: Perspective of Survivors
Bibliographic record
Abstract
OBJECTIVES: Our understanding of elder abuse (EA) phenomena has largely been shaped from the perspective of researchers and professionals whose conceptualizations often differ from the perceptions of older adults who experience mistreatment. This study sought to understand the most distressing aspects of EA victimization from the perspective of survivors. METHODS: = 32) of EA survivors, recruited from EA support and Adult Protective Services programs in New York City and Los Angeles. Analysis followed a constant comparison process involving two independent coders to understand distressing aspects of EA victimization. RESULTS: The following themes emerged as the most distressing aspects of EA victimization: fear, disbelief, disrespect, concern for perpetrator and other family members, feelings of loss, and incongruity between survivor wishes and systemic responses. Distressing aspects of EA victimization spanned personal, relational, and systemic levels of ecological influence. CONCLUSIONS: Findings from this study advance basic knowledge on EA phenomena and carry direct implications for programs designed to support and meet the needs of survivors. CLINICAL IMPLICATIONS: Findings identify particularly distressing psycho-emotional aspects of EA victimization for clinicians interacting with survivors that can serve as targets of intervention.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".