Factors associated with internet gambling in university students / by Nicholas M. Harris.
Bibliographic record
Abstract
Problem gambling can have devastating financial, psychological, and social effects on the individual and the individual?s family. Over the past 15 years, Internet gambling has grown at an incredible pace and is believed to be a contributing factor to the increase in problem and pathological gamblers. Of particular concern is the increase in problematic gambling behaviours \nin university students. Research shows that university and college students engage in gambling more than any other group. The etiology of problem and pathological gambling is unknown, but is likely the result of numerous biological, psychological, and environmental factors. The current study examined factors related to problem Internet gambling in a university student sample (N = 325). Measures administered included the South Oaks Gambling Screen (SOGS), the DSM-lV-TR-Based Questionnaire (DBQ), the Canadian Problem Gambling Index (CPGI), three scales from the Jackson Personality Research Form (PRF) (Impulsivity, Desirability, and Infrequency), and a questionnaire examining gambling and Internet gambling behaviours and attitudes. Results showed that Internet gamblers were significantly more likely than non-Internet gamblers and non-gamblers to report engaging in high risk behaviours such as alcohol use, tobacco use, and marijuana use. Many Internet gamblers reported that Internet gambling negatively affects their academic achievement and some reported that it affects their class attendance. Internet gamblers were more likely than non-Internet gamblers to have reported having a family member with a past or current gambling problem, but no differences were found between Internet problem gamblers and Internet non-problem gamblers on this variable. Furthermore, as hypothesised, (1) Males were significantly more likely to gamble on the Internet, significantly more likely to meet DSM-IV-TR criteria for pathological gambling, and significantly more likely to meet CPGI criteria for problem gambling. However, males were not found to be significantly more likely than females to meet SOGS criteria for problem gambling. (2) Internet gamblers were significantly more likely to meet DSM-IV-TR criteria for pathological gambling and meet SOGS and CPGI criteria for problem gambling and (3) Internet gamblers were significantly more likely than non-Internet gamblers to report trusting the Internet gambling \nindustry. Finally, although impulsivity was not significantly correlated with problem gambling among all Internet gamblers, a significant positive correlation was found between impulsivity and problem Internet gambling behaviours among males. \nStudy population : Lakehead University students (Thunder Bay, Northwestern Ontario)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".