Mahjong and Problem Gambling in Sydney: An Exploratory Study with Chinese Australians
Bibliographic record
Abstract
Gambling is accepted as an integral part of Chinese cultural heritage. Epidemiological and clinical studies indicate that problem gambling rates among Chinese community members residing in Western countries are substantially higher (2.1-2.9%) compared with those reported for mainstream populations (0.5-1.7%). However, these studies failed to differentiate culturally specific forms of gambling and their association with problem gambling within Chinese samples. Thus, it is not possible to determine if, or what proportion of, Chinese problem gamblers exhibit a propensity to experience problems with culturally specific, as opposed to mainstream, forms of gambling. Mahjong, a popular game deeply entrenched in Chinese tradition, is played among peers and family members. In a recent study conducted by Zheng, Walker, and Blaszczynski (2008), high rates of Mahjong-associated problem gambling were found in a sample of Chinese international students attending language schools and universities in Sydney, Australia. The aim of the current study was to explore the extent of Mahjong-associated problem gambling in a broader community sample of Chinese Australians. Results showed that in a sample of 229 respondents, males and those 35 years or older were more likely to gamble on Mahjong and that 3.1% met the Canadian Problem Gambling Severity Index criteria for Mahjong problem gambling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".