Senior gambling in Hong Kong: through the lenses of Chinese senior gamblers – an exploratory study
Bibliographic record
Abstract
The meanings of gambling among senior gamblers in Hong Kong were investigated using semi-structured interviews based on an ethnographic approach. 18 senior gamblers (10 men; 8 women) over the age of 55 years were asked to describe their childhood, adolescent and early adult experience and developmental history of gambling and gambling trajectories. They also completed the Problem Gambling Severity Index (PGSI) of the Canadian Problem Gambling Index. Most senior gamblers ( n = 15) were non-problem gamblers, except 3 participants who were classified as pathological gamblers. The majority of the senior gamblers began their lifelong gambling career when they were young. Their family members often introduced the participants to gambling. Some participants reported that an early big win was a focal memorable experience in their early gambling history. Women played mahjong most frequently, whereas men gambled on horse races and sports betting such as football lotteries. The main motivation of gambling for older adult women was socialisation with friends, whereas older adult men were motivated to gamble because of potential financial gain. To senior women, games of mahjong with friends have provided an oasis and a comfort zone, within which they can find peace and comfort away from hustles of daily life. Cultural conditions in Hong Kong and their link to senior gambling have been also discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".