The Windsor Gambling Screen for Older Adults: Initial Steps in Developing a Gambling Screen for Older Adults
Bibliographic record
Abstract
While concerns have been raised about the potential impacts of gambling on older adults (Shaffer, Hall, and Van der Bilt,1997), research has been hampered by a lack of adequate measures of problem gambling for older adults (Wiebe, 2002; Windsor Problem Gambling Research Group, 2003). The current study focussed on developing a problem gambling screen for adults age 55 and over. During Phase I of the research, focus groups were conducted with 52 older adult gamblers and family members. Focus groups yielded 19 items suitable for a draft screen. In Phase II, 188 participants 55 and over with varying degrees of gambling involvement completed a questionnaire assessing the discriminant validity of the draft screen. Participants provided demographic information and responses to the Canadian Problem Gambling Index (CPGI; Ferris and Wynne, 2001) and the draft screen. Discriminant Function Analysis used screen items to predict CPGI group membership (No Problem versus At Risk). Nine screen items best predicted gambling risk in older adults. Additional validation was obtained through analysis of clinical interviews with Moderate Risk and Problem gamblers in Phase III. The 9-item Windsor Gambling Screen for Older Adults provides a promising start to better identifying older adults at risk of problem gambling.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".