Bibliographic record
Abstract
The present study investigated the older adults’ unique vulnerability to fraud and scams by understanding the role of social, cognitive, and knowledge-based factors, and testing a prevention paradigm. Studies 1 and 2 built upon preliminary work by examining social and cognitive predictors of prior victimization in community dwelling Chinese older adults. Study 3 examined the most promising factors again in a Canadian sample. The results here suggest that older adults in China who experience more socioemotional disturbance due to isolation may be at higher risk of victimization compared to their peers and compared to those in Canada. To better understand vulnerability, an objective scam detection task was designed for studies 4 and 5. Study 4 examined predictors of detection ability in older and younger adults and found a combined role of cognitive processing and accumulated knowledge after controlling for social and demographic factors. Study 5 used these findings to test a training with feedback paradigm to improve detection ability. Immediately after training, the intervention group performed significantly better than the control group, while the alternative education intervention group did not. However, the gains declined significantly by the three-week follow-up. Collectively, the findings support the need for age-based investigations of fraud vulnerability, the need for objective behavioural measures, and the possibility of knowledge-based interventions albeit with additional improvements.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".