FACILITATORS AND BARRIERS FOR ELDER ABUSE VICTIMS SEEKING HELP: FINDINGS FROM THE NATIONAL ELDER MISTREATMENT STUDY
Bibliographic record
Abstract
Abstract Understanding help-seeking among victims of elder abuse is a critical challenge in the field. The vast majority of elder abuse victims remain hidden from formal support/protective response systems, such as adult protective services, legal/justice, law enforcement, or other agencies responsible for addressing this issue in the community. Guided by the Behavioral Model of Health Services Use, this study examined factors that facilitate or impede formal help-seeking among victims of elder emotional, physical and sexual abuse, represented by a call for help in the form of a report to police or other authorities. Data came from a national, population-based elder abuse study in the U.S. with a representative sample (n=304) of victims reporting abuse in the past year. Gold-standard measurement strategies were used to assess each elder abuse subtype. Multivariable logistic regression was conducted to identify help-seeking facilitators/barriers. Help-seeking through reporting to police or other authorities occurred among only 15.4% of elder abuse victims nationwide. Help-seeking was predicted by factors attached to the victim (abuse type, poly-victimization), perpetrator (prior police trouble, social network size), and victim-perpetrator relationship (victim dependence on perpetrator). This study highlights the extremely hidden nature of elder abuse in our society, as well as the need to develop strategies that incorporate victim, perpetrator, and victim-perpetrator relationship factors to promote greater help-seeking among victims.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".