Correlates of gambling-related problems among older adults in Ontario
Bibliographic record
Abstract
Although the literature suggests that gambling among older adults is influenced by unique age-related factors, there is little information on the factors associated with the experience of gambling-related problems among older adults. The purpose of this study was to identify the sociodemographic health determinants and mental health-related problems, including alcohol and drug dependence, that are associated with the experience of gambling problems among older adults in Ontario. The research was an exploratory analysis of data from Ontario adults, aged 55 and over, who completed the Canadian Community Health Survey -Mental Health and Well-being, Cycle 1.2 (1,904 males and 2,622 females). Logistic regression analyses were conducted to identify sociodemographic, gambling behaviour, and mental health correlates of the experience of any gambling-related problems, as identified by responses to the Canadian Problem Gambling Index. Being married or living common law and having a higher education level were associated with reduced risk of gambling problems. Among mental health variables, alcohol dependence and any substance dependence significantly increased the odds of reporting a gambling problem. Gambling behaviour measures, such as more frequent gambling, participating in more types of gambling, and spending more on gambling were significant correlates of gambling problems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".