Identity Theft and Older Adults: How Minorities and the Poor Suffer the Worst Consequences
Bibliographic record
Abstract
Abstract Society’s growing reliance on technology to transfer and store private information has created more opportunities for identity thieves to access personal data. Prior work using data from the National Crime Victimization Survey (NCVS) Identity Theft Supplement (ITS) showed that baby boomers were significantly more likely than Millennials to be victims of identity theft and that older people and minorities experience more severe economic and psychological consequences. This study examines how socioeconomic status, demographic characteristics, and incident-specific factors relate to how much money is stolen during identity theft, the likelihood of experiencing out-of-pocket costs, and emotional distress among identity theft victims age 65 and older. Using combined data from the 2014 and 2016 NCVS-ITS, this study examines the correlates of financial and psychological consequences of identity theft among 2,307 victims age 65 and older. Older Black victims are more likely to have greater amounts of money stolen and are more likely feel distressed than older non-Latino white identity theft victims. The most disadvantaged older adults living at or below the federal poverty level are nearly five times as likely to suffer out-of-pocket costs. The length of time information is misused and the hours spent resolving identity theft are significantly associated with emotional distress. More than one-third of older victims experience moderate to severe emotional distress following identity theft, and those who can least afford it suffer out-of-pocket costs. Greater advocacy and psychological support are needed to help older adults recover, in addition to tools to protect their personal information from misuse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".