Adolescent Gambling and Problem Gambling: A New Zealand Study
Bibliographic record
Abstract
The prevalence of gambling and problem gambling among adolescents in New Zealand has not been adequately investigated. Prospective studies of current underage gambling may be unreliable, because respondents may fear self-incrimination. In this retrospective study, a non-representative sample of 68 first year psychology students, between the ages of 15 and 24 years, completed a questionnaire which asked them to recall their gambling activities before the age of 20 years, and which included the South Oaks Gambling Screen (SOGS). In adolescence, the entire sample had gambled for money at least once, and 18% regularly. Participants who played housie (bingo), gambled in casinos, or bought Lotto tickets, had the highest spending rates. 13% of the sample was classified as problem gamblers and 5% probable pathological gamblers in adolescence. Activities associated with pathological gambling included scratch tickets, gaming machines and housie. Regular gambling significantly predicted problem gambling scores. The results were compared with findings from a national sample and adolescent samples overseas. Recent research in the United States, Canada and the United Kingdom (Buchta, 1995; Fisher, 1993; Jacobs, 1989, cited in Winters, Stinchfield, & Kim, 1995; Ladouceur, Dube, & Bujold, 1994; Lesieur et al., 1991; Shaffer & Hall, 1996) indicates that prevalence rates for adolescent gambling and for adolescent problem gambling are higher than for adult populations. Estimates of adolescent problem gambling range from one and a half to more than three times as high as for the adult population. The vast majority (85 to 95%) of adolescents have gambled for money at least once. Prevalence of gambling and problem gambling among adolescent males is usually greater than among female adolescents (Buchta, 1995; Jacobs, 1989, cited in Winters et al., 1995; Ladouceur et al., 1994; Winters, Stinchfield, & Fulkerson, 1993a, 1993b). Adolescent males may spend more than adolescent females on lottery tickets (Browne & Brown, 1994; Ladouceur et al., 1994) but not on fruit machines (Fisher, 1993). For some adolescent problem gamblers, gambling is associated with a number of problems. First, research suggests that a direct relationship exists between age of onset of underage gambling and the severity of future gambling problems (Abbott & Volberg, 1996; Bergh, & Kuhlhorn, 1994; Fisher, 1993; Griffiths, 1995a, 1995b; Ladouceur and Mireault, 1988; Stinchfield & Winters, 1994, cited in Winters, et al., 1995). Second, empirical investigations have consistently shown that adolescent pathological gambling is positively related to the use of alcohol, tobacco and addictive substances, to substance abuse, and to criminality including stealing, drug pushing, and prostitution (Arcuri, Lester, & Smith, 1985; Browne & Brown, 1994; Fisher, 1993; Griffiths, 1990, Jacobs, 1987; cited in Radecki, 1994; Ladouceur et al., 1994; Lesieur & Heineman, 1988, cited in Buchta, 1995; Shaffer & Hall, 1996; Winters, et al., 1993b). Other problems include suicide attempts, disruptions to relationships with family and friends, and educational difficulties such as truancy and poor academic performance (Browne & Brown, 1994; Fisher, 1993; Griffiths, 1990; Ladouceur et al., 1994; Shaffer, LaBrie, Scanlan, & Cummings, 1994; Winters et al., 1993b). In the United Kingdom, Fisher (1993) found that 10% of adolescents who played on fruit machines had fallen out with close friends and family as a direct consequence of their gambling. However, the causal relationship between gambling and family problems could be the reverse. Another study found that the majority of adolescent gambling machine players reported that family problems precipitated their addiction (Dickerson, 1984, cited in Bentall, Fisher, Kelly, Bromley, & Hawksworth, 1989). The severity of adolescent gambling problems is associated with gambling regularly and with the amount of money spent (Winters et al. …
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".