DSM‐IV Diagnostic Criteria for Pathological Gambling: Reliability, Validity, and Classification Accuracy
Why this work is in the frame
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Bibliographic record
Abstract
The purpose of this study was to examine the reliability, validity, and classification accuracy of the DSM-IV diagnostic criteria for pathological gambling. Given the lack of a laboratory test to diagnose pathological gambling, two groups were recruited in order to test DSM-IV diagnostic classification accuracy, one which likely had the disorder and the other which likely did not have the disorder (121 men and women clients at a gambling treatment facility) (138 men and women selected at random from the Windsor, Ontario, community who had gambled in the past twelve months). The Gambling Behavior Interview was administered to both groups. The Gambling Behavior Interview was administered to both groups. The Gambling Behavior Interview includes items that measure the ten DSM-IV diagnostic criteria for pathological gambling as well as other gambling problem severity measures and scales that served as tests of convergent validity. The ten DSM-IV diagnostic criteria were found to exhibit satisfactory reliability, validity, and classification accuracy; however, lowering the cut score to four and using item weights yielded improved classification accuracy over the standard cut score of five. Some diagnostic criteria were found to have greater discriminatory power than other criteria. The results of this study suggest that the classification accuracy of DSM-IV diagnostic criteria can be improved upon with a lower cut score or using weighted criteria.
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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 | 0.001 |
| 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) | 0.001 | 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 it