Financial fraud and health: the case of Spain
Bibliographic record
Abstract
OBJECTIVE: To examine whether financial fraud is associated with poor health sleeping problems and poor quality of life. METHODS: Pilot study (n=188) conducted in 2015-2016 in Madrid and León (Spain) by recruiting subjects affected by two types of fraud (preferred shares and foreign currency mortgages) using venue-based sampling. Information on the monetary value of each case of fraud; the dates when subjects became aware of being swindled, lodged legal claim and received financial compensation were collected. Inter-group comparisons of the prevalence of poor physical and mental health, sleep and quality of life were carried according to type of fraud and the 2011-2012 National Health Survey. RESULTS: In this conventional sample, victims of financial fraud had poorer health, more mental health and sleeping problems, and poorer quality of life than comparable populations of a similar age. Those who had received financial compensation for preferred share losses had better health and quality of life than those who had not been compensated and those who had taken out foreign currency mortgages. CONCLUSION: The results suggest that financial fraud is detrimental to health. Further research should examine the mechanisms through which financial fraud impacts health. If our results are confirmed psychological and medical care 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".