How much does that cost? Examining the economic costs of crime in North America attributable to people with psychopathic personality disorder.
Bibliographic record
Abstract
Cost of illness research has established that mental disorders lead to significant social burden and massive financial costs. A significant gap exists for the economic burden of many personality disorders, including psychopathic personality disorder (PPD). In the current study, we used a top-down prevalence-based cost of illness approach to estimate bounded crime cost estimates of PPD in the United States and Canada. Three key model parameters (PPD prevalence, relative offending rate of individuals with PPD, and national costs of crime for each country) were informed by existing literature. Sensitivity analyses and Monte Carlo simulations were conducted to provide bounded and central tendency estimates of crime costs, respectively. The estimated PPD-related costs of crime ranged from $245.50 billion to $1,591.57 billion (simulated means = $512.83 to $964.23 billion) in the United States and $12.14 billion to $53.00 billion (simulated means = $25.33 to $32.10 billion) in Canada. These results suggest that PPD may be associated with a substantial economic burden as a result of crime in North America. Recommendations are discussed regarding the burden-treatment discrepancy for PPD, as the development of future effective treatment for the disorder may decrease its costly burden on health and justice systems. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".