Endorsement of Criminal Behavior Amongst Offenders: Implications for DSM-5 Gambling Disorder
Bibliographic record
Abstract
The fifth edition of the diagnostic and statistical manual (DSM) has changed the scoring threshold for a gambling disorder (GD) from five criteria to four and eliminated the illegal acts criterion. The impact of these changes was examined with data from a correctional population (N = 676) in Ontario, Canada. The offenders completed a self-report survey that included the Canadian problem gambling index, the South Oaks Gambling Screen and the DSM-IV criteria. Changing the threshold from 5 to 4 improved the convergent validity for GD and resulted in an increase in the percentage of offenders diagnosed with a GD from 7.4 to 10.2 %. The results also indicate that the illegal acts criterion contributes to the convergent validity of GD. The evidence supports the change in the threshold from five to four, but also reinforces the importance of examining illegal acts when dealing with an offender population. The incorporation of illegal acts into the "lying to others" criteria appears to make up, to some extent, for the removal of the illegal acts criterion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".