Gambling in older adults: Factors associated with problem gambling behaviour in older adults.
Bibliographic record
Abstract
Increase in the accessibility and social acceptance of gambling activities and venues has resulted in a significant rise in the number of individuals gambling. Older adults in particular appear to be gambling at a much higher rate, although relatively little research has examined this population with respect to gambling. The purpose of this study was to examine the etiological, social, emotional, and situational variables related to the onset and maintenance of problem gambling in older adults. Specifically, the relationship between depression, stress, coping styles, health, dissociation, and childhood trauma, and gambling seventy were investigated from the frameworks of Jacobs' General Theory of Addictions. This study also examined whether or not early dissociative states as a result of childhood trauma predict a relationship between later dissociation during gambling and whether or not dissociation was related to degree of problem gambling. Ninety-one older adults (age 55+) were recruited from a treatment group and from the general population and completed several measures. A subset of the sample also participated in semi-structured interviews allowing for a qualitative analyses. Results indicated that 30.8% of the sample scored in the Moderate/High Risk to Problem Gambling range. This group, compared with No-to-Low Risk gamblers, demonstrated significantly higher scores on measures of depressive symptomatology, perceived stress, health difficulties, negative coping strategies and dissociation. Regression analyses indicated that the combination of dissociation during gambling, emotion-focused coping, depressive symptomatology and the interactive effects of childhood trauma significantly predicted gambling severity scores. Qualitative investigation suggested that negative life-events, depression, and the desire to escape were involved in both the onset and maintenance of problem gambling behaviour. Findings are interpreted and discussed with respect to theories in the literature and implications for identification, prevention, and intervention with older problem gamblers.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .L355. Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3692. Adviser: G. R. Frisch. Thesis (Ph.D.)--University of Windsor (Canada), 2004.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".