GAMBLING AMONG CULTURAL MINORITY OLDER ADULTS: A MIXED METHODS SYSTEMATIC REVIEW
Bibliographic record
Abstract
Despite growing numbers of older gamblers, many of whom are cultural minority group members, there has been a lack of systematic reviews of gambling among this particular population. This is the first systematic review of empirical evidence in the literature on how gambling has been affecting older adults from cultural minorities. This review applied a mixed methods approach to examine both quantitative and qualitative studies published between 1996 and 2016. A thorough search of seven databases yielded 17 articles with a total sample of 9,044 older gamblers of cultural minority backgrounds. Eleven were quantitative studies and six were qualitative (including one mixed methods study). Nine studies (53%) focused or included Asian seniors, five (29%) on African-American older adults, four (24%) included indigenous populations, and one (6%) included a Hispanic sample. Lifetime gambling prevalence rates range from 26.6% to 92%, while problem gambling rates from 2.2% to 17%. Onset ages vary from as early as 7 or 8 to 69. Causes of gambling include enabling (environmental) factors and motivational (personal) factors. Enabling factors are cultural acceptance, socializing/bonding power of gambling, social networks, accessibility (availability, transportation, and low entry barriers), and external stimuli. Motivational factors include intentions of reducing boredom, increasing socialization, enjoying freedom, reducing stress, and winning money. Both categories of factors can also act as buffers that hinder gambling behavior. Findings on consequences of gambling, personal coping strategies, and intervention practice are also synthesized. The review is concluded with a critical discussion of findings, gaps in research, and suggestions for future investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".