The Financial and Psychological Impact of Identity Theft Among Older Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Society's growing reliance on technology to transfer private information has created more opportunities for identity thieves to access and misuse personal data. Research on identity theft specifically among adults aged 65 and older is virtually nonexistent, yet research focusing on victims of all ages indicates a positive association between age, minority status, and more severe economic and psychological consequences. RESEARCH DESIGN AND METHODS: Identity theft measures come from a sample of more than 2,000 self-reported victims aged 65 and older from the nationally representative National Crime Victimization Survey Identity Theft Supplements administered in 2014 and 2016. Regression was used to examine how socioeconomic status, demographic characteristics, and incident-specific factors relate to how much money is stolen, the likelihood of experiencing out-of-pocket costs, and emotional distress among older identity theft victims. RESULTS: Older Black identity theft victims were more likely to have greater amounts of money stolen and were more likely to feel distressed by the incident than older White victims. The most disadvantaged older adults living at or below the federal poverty level were significantly more likely to suffer out-of-pocket costs. The length of time information was misused, experiencing subsequent financial problems and problems with friends/family, and the hours spent resolving identity theft were positively associated with emotional distress. Among those aged 65 and older, age was not significantly associated with losses or emotional distress. DISCUSSION AND IMPLICATIONS: Older adults living in poverty need more resources to assist with recovery and reporting identity theft to law enforcement. Limiting the extent of losses from identity theft and reducing the length of time information is misused may reduce the emotional toll of identity theft on older victims.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".