Subtypes of disordered gamblers: results from the <scp>N</scp>ational <scp>E</scp>pidemiologic <scp>S</scp>urvey on <scp>A</scp>lcohol and <scp>R</scp>elated <scp>C</scp>onditions
Bibliographic record
Abstract
AIMS: To derive empirical subtypes of problem gamblers based on etiological and clinical characteristics described in the Pathways Model, using data from a nationally representative survey of US adults. DESIGN & MEASUREMENT: Data were collected from structured diagnostic face-to-face interviews using the Alcohol Use Disorder and Associated Disabilities Interview Schedule DSM-IV version IV (AUDADIS-IV). SETTING: The study utilized data from US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). PARTICIPANTS: All disordered gambling participants (n = 581) from a nationally representative cross-sectional sample of civilian non-institutionalized adults aged 18 years or older. FINDINGS: Latent class analyses indicated that the best-fitting model was a three-class solution. Those in the largest class (class 1: 50.76%, n = 295) reported the lowest overall levels of psychopathology including gambling problem severity and mood disorders. In contrast, respondents in class 2 (20.06%, n = 117) had a high probability of endorsing past-year substance use disorders, moderate probabilities of having parents with alcohol/drug problems and of having a personality disorder, and the highest probability for past-year mood disorders. Respondents in class 3 (29.18%, n = 169) had the highest probabilities of personality and prior-to-past year mood disorders, substance use disorders, separation/divorce, drinking-related physical fights and parents with alcohol/drug problems and/or a history of antisocial personality disorder (ASPD). CONCLUSIONS: Three subtypes of disordered gamblers can be identified, roughly corresponding to the subtypes of the Pathways Model, ranging from a subgroup with low levels of gambling severity and psychopathology to one with high levels of gambling problem severity and comorbid psychiatric disorders.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".