Decision making under risk and pathological gambling in older adults
Bibliographic record
Abstract
Although previous research has suggested that decision making deficits characterized \npathological gamblers, this relationship has not been yet investigated among older adults. Our \nstudy was aimed at investigating the role of decision-making under risk on pathological gambling \nin people aged 65 years and over by controlling for their cognitive functioning. Participants were \n54 older adults (100% males, age range: 65-89 years, Mean age=74.29, SD=6.49) attending \nrecreational clubs in Italy. The Game of Dice Task (GDT) was used to measure risky decision making, and the Canadian Problem Gambilng Index (CPGI) was administered to relieve \npathological gambling. Older adults’ cognitive functioning was measured with the Montreal \nCognitive Assessment (MoCA). To investigate the effect of risky decision making on pathological \ngambling, an ANOVA was conducted using the CPGI score as the dependent variable and risky \ndecision making category, i.e., Non-risk decision makers, Low-risk decision makers, and High-risk \ndecision makers, as the independent variable. Controlling for cognitive functioning, results showed \na significant effect of risky decision making category (F(2,41)=3.84, p<.05, η2=.16). In detail, the \nCPGI score of High-risk decision makers was significantly higher (p<.001) than that obtained by \nNon-risk decision makers. This study suggests that the tendency to make disadvantageous choices \nin a situation of decision making under risk could make older adults more at-risk for the \noccurrence of problematic behaviors related to 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".