DSM‐IV Diagnostic Criteria for Pathological Gambling: Reliability, Validity, and Classification Accuracy
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".