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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".