Gambling among culturally diverse older adults: a systematic review of qualitative and quantitative data
Bibliographic record
Abstract
Culturally diverse older gamblers may face multiple jeopardies and socially structured challenges. In this first systematic review of empirical evidence of gambling in this population, the authors examined both quantitative and qualitative studies published between 1996 and June 2016. A thorough search of 7 databases yielded 18 articles with a total sample of 11,296 culturally diverse older adults. The review revealed contrary findings on the correlation between gender, education, income and gambling behaviour. Early onset was more frequently found among older adults who belonged to a culture that promoted tolerance of gambling activities; however, some developed a gambling habit after they had moved to a western society. Using an analytical framework, the authors demonstrate interrelated factors: enabling factors (cultural acceptance of gambling, supportive social networks, accessibility to gambling facilities and venues, and external cues); motivational factors (desire for excitement and winning money, coping with boredom, and stress due to structural issues); and buffering factors for culturally diverse older gamblers . Both environmental and personal factors could be triggered as buffers between gambling and culturally diverse older adults. In light of the motivational and enabling factors, practitioners and policy makers may need to step beyond focusing on ‘correction’ during intervention.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".