Once online poker, always online poker? Poker modality trajectories over two years
Bibliographic record
Abstract
Online poker is considered more at-risk than land-based poker in terms of intense gambling behaviors and gambling problems. The development of many online gambling sites has raised public health concerns about the potential increase in online poker players. Longitudinal studies are useful to better understand the evolution of gambling behaviors; however, very few consider online poker players. Using a prospective design, this study aims to identify online and land-based trajectories over a two-year period and the factors influencing those trajectories. Results are based on data collected at three time-points over the course of a prospective cohort study conducted in Quebec (n = 304). A latent class growth analysis was performed to determine trajectories based on the main poker modality played, either online or land-based poker. Multinomial multivariable logistic regression analyses were conducted to determine the correlates of poker playing trajectories. Over two years, three poker playing trajectories were identified, comprising two stable trajectories [stable land-based (51.5%) and stable online (36.3%)] and an unstable trajectory [unstable online land-based (12.1%)]. The second trajectory included online poker players at baseline who transitioned to land-based poker. Number of gambling activities increased the odds of being in the first trajectory as compared to the others. Severity of gambling problems was a significant predictor of the second "unstable" or the third "stable online" trajectories, but not for the first "stable land-based" poker trajectory. The majority of poker players remained in either the land-based or online trajectories over two years. No poker players transitioned from land-based to online poker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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 teacher head, 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".