Trusting problem gamblers: Reliability and validity of self-reported gambling behavior.
Bibliographic record
Abstract
The retest reliability and validity of self-reported gambling behavior were assessed in 2 samples of problem gamblers. Days gambled and money spent gambling over a 6-month timeframe were reliable over a 2- to 3-week retest period using the timeline follow-back interview procedure (N=35; intraclass correlation coefficients [ICCs] ranged from .61 to .98). Gamblers did, however, report significantly more gambling at the 2nd interview. Agreement with collaterals was fair to good overall (ICCs ranged from.46 to.65) with no clear pattern of either over- or underreporting by gamblers. Spouses did not show greater agreement with gamblers compared with nonspouses, and greater agreement was not found for collaterals who were more versus less confident in their reports. The results are generally supportive of the use of self-reported gambling in studies of problem gamblers, assessed face to face and by telephone, although suggestions for further research are provided.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".