Alexithymia in Young Adulthood: A Risk Factor for Pathological Gambling
Bibliographic record
Abstract
BACKGROUND: Pathological gambling is more prevalent among postsecondary students than among the general adult population. While the prevalence of pathological gambling in this group has risen over the past decade, factors underlying the development of problem gambling among university students remain largely unexplored. One early study found alexithymia to be associated with pathological gambling. The aim of the present study was to further examine the relationship between alexithymia and gambling among postsecondary students. METHODS: The relationship between alexithymia and pathological gambling was examined in 562 postsecondary students who completed the South Oaks Gambling Screen (SOGS) and the 20-item Toronto Alexithymia Scale (TAS-20). RESULTS: Approximately 12% of the sample was classified as alexithymic according to the TAS-20. These individuals were found to have significantly more gambling problems, as measured by the SOGS, than nonalexithymic individuals. Approximately 9% of the sample was classified as pathological gamblers according to the SOGS. These individuals were found to have significantly higher levels of alexithymia, as measured by the TAS-20, than nonproblem gamblers. CONCLUSIONS: Alexithymia is associated with pathological gambling and may be a risk factor among postsecondary students for developing severe gambling problems.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".