Understanding Service Utilization in Cases of Elder Abuse to Inform Best Practices
Bibliographic record
Abstract
Elder abuse (EA) case resolution is contingent upon victims accepting and pursuing protective service interventions. Refusal/underutilization of services is a major problem. This study explored factors associated with extent of EA victim service utilization (SU). Data were collected from a random sample of EA cases (n = 250) at a protective service program in New York City. In cases involving financial abuse, higher SU was associated with females, poor health, perceived danger, previous help-seeking, and self or family referral. In physical abuse cases, higher SU was associated with family referral and previous help-seeking; lower SU was related to Hispanic race/ethnicity, being married, and child/grandchild perpetrator. In emotional abuse cases, higher SU was associated with self or family referral, victim-perpetrator gender differential, perceived danger, and previous help-seeking; lower SU was related to child/grandchild perpetrator. Findings carry implications for best practices to retain and promote service use among elder victims of 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".