Pathological and disordered gambling: a comparison of DSM-IV and DSM-V criteria
Bibliographic record
Abstract
The proposed revision of the diagnostic criteria for pathological gambling within the DSM suggests removing the criterion of committing illegal acts and reducing the threshold to four symptoms. It has been argued that changing the diagnostic criteria will not impact the prevalence rate of pathological gambling, however there are no published studies examining prevalence rate stability. The impact of the proposed DSM-V criteria using data from a national study assessing gambling behaviors among college student-athletes was examined. Comparison of pathological or disordered gamblers vs sub-threshold gambling severity using current DSM-IV criteria and the proposed DSM-V diagnostic criteria suggests that the proportion of men classified as pathological or disordered gamblers changes. For females, comparisons did not reach statistical significance. The subcommittee of the DSM-V should note that the proportion of males meeting the diagnostic criteria for pathological gambling may be influenced by classification system. Questions related to the validity of the proposed classification system are raised.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".