The Role of Cognition, Personality, and Trust in Fraud Victimization in Older Adults
Bibliographic record
Abstract
Older adults are more at risk to become a victim of consumer fraud than any other type of crime (Carcach et al., 2001) but the research on the psychological profiles of senior fraud victims is lacking. To bridge this significant gap, we surveyed 151 (120 female, 111 Caucasian) community-dwelling older adults in Southern Ontario between 60 and 90 years of age about their experiences with fraud. Participants had not been diagnosed with cognitive impairment or a neurological disorder by their doctor and looked after their own finances. We assessed their self-reported cognitive abilities using the MASQ, personality on the 60-item HEXACO Personality Inventory, and trust tendencies using a scale from the World Values Survey. There were no demographic differences between victims and non-victims. We found that victims exhibit lower levels of cognitive ability, lower honesty-humility, and lower conscientiousness than non-victims. Victims and non-victims did not differ in reported levels of interpersonal trust. Subsequent regression analyses showed that cognition is an important component in victimization over and above other social factors. The present findings suggest that fraud prevention programs should focus on improving adults' overall cognitive functioning. Further investigation is needed to understand how age-related cognitive changes affect vulnerability to fraud and which cognitive processes are most important for preventing fraud victimization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".