Gambling and Aging: An Overview of a Risky Behavior
Bibliographic record
Abstract
Gambling is a field of study that has grown since the 2000s. Much research has focused on adolescents and youth as a vulnerable population. The rate of aging gamblers is increasing; however, evidence-based knowledge of this population is still too sparse. After introducing the issue (1), this article provides a narrative review of older adults' gambling through three sections: (2) older adult gamblers (age, characteristics, and motivations), (3) gambling as a risky decision-making situation, and (4) gambling disorder related to older adults. By drawing on the existing literature from a problematization perspective, this type of review can highlight complex and original research topics and provoke thought and controversy to generate avenues for future research. This narrative review provides an overview of the existing literature on gambling among older adults and offers perspectives on how aging can affect decision-making and thus gambling for this population. Older adults are a specific population, not only in terms of the consequences of gambling disorders but also in terms of the motivations and cognitions underlying gambling behaviors. Studies on behavioral science focusing on decision-making in older adults could help in the development of public policy in terms of targeted prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".