PREVALENCE OF ELDER FINANCIAL FRAUD AND SCAMS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Elder financial exploitation research to date has largely focused on scenarios occurring within relationships of trust (e.g., family). Little is known about elder financial fraud and scam (EFFS) forms of exploitation perpetrated by strangers, including foundational prevalence knowledge. This paper presents on EFFS prevalence estimation in the United States based on a systematic review and meta-analysis of state- and national-level population-based studies. Systematic review of the literature using multiple screeners/reviewers resulted in 12 eligible studies. To estimate EFFS prevalence, meta-analysis used generalized mixed modeling containing binomial error assumption and a logistic link function, with studies included as levels of a random classification factor. Overall EFFS prevalence (one- to five-year period) was 5.64% (95%CI: 4.04%-7.83%). Sub-analysis on study methodological differences revealed no significant effect on prevalence estimation. This study provides the most valid EFFS prevalence estimate to date, which is a necessary foundational piece for further research on the topic.
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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".