Of time and <italic>The Chase</italic>: Lifetime versus past-year measures of pathological gambling
Bibliographic record
Abstract
Objective: This analysis tested whether past-year measures can be shown to have methodological advantages over lifetime measures of pathological gambling based on DSM-IV criteria. Methods: Two stratified random-sample surveys (n=2,417, n=530) of gambling behavior and correlates were conducted with community-based U.S. adults. A fully structured questionnaire, administered by trained interviewers, screened for lifetime and past-year prevalence of the 10 DSM-IV criteria for pathological gambling. Sample: The study sample comprised 1,216 gamblers who were administered the pathological gambling screen, with particular attention given to the 400 gamblers who reported one or more gambling-related problems. Results: Pathological gambling criteria as measured by lifetime items showed greater consistency with past-year items than was true for other levels of gambling problems. Neither lifetime nor past-year measures were positively related to the age of the respondent. Conclusion: These findings deny the presumptively greater accuracy of past-year over lifetime measures of pathological gambling based on DSM-IV criteria in prevalence studies in the general population. In view of greater conceptual fidelity to DSM-IV concepts, lifetime measures appear preferable to past-year.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".