FINANCIAL FRAUDS AND THE HEALTH OF MIDDLE-AGED AND OLDER ADULTS: THE CASE OF SPAIN
Bibliographic record
Abstract
Background: Globally, financial frauds cause loss of lifetime savings to millions of small savers. Whether financial frauds have harmful effects on health has not yet been explored. Our objective was to examine whether fraudulent behaviors by financial institutions are associated with physical and mental health problems in affected populations, comparing with the health of the general population to which they belong. Methods: Pilot study (n=188) conducted in 2015 in the central region of Spain by recruiting subjects affected by two major types of frauds (preferred shares and foreign currency mortgages) using venue-based sampling. Information about monetary value of fraud, dates for awareness of fraud, legal claim and financial compensation were collected. Comparisons of means and prevalence of physical and mental health indicators, sleep and quality of life were carried out between groups by type of fraud and the 2011–2012 National Health Survey. Results: In this conventional sample, victims of financial fraud had worse health, more sleep problems and worse quality of life than comparable populations of similar age. Those who had received financial compensation for lost savings in preferred shares had significantly better health and quality of life than those who had not been compensated and those who contracted foreign currency mortgages. Conclusion: This pilot research suggests harmful effects of financial frauds on the health of those affected. Further research could examine the mechanisms through which financial frauds impact public health. If these pilot results are confirmed, psychological and medical assistance should be provided, in addition to financial compensation.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".