Inventaire des cognitions à risque — Loteries sportives (ICR-LS) : un premier pas dans l’élaboration de mesures spécifiques au type de jeu/The Inventaire des cognitions à risque Loteries Sportives: A First Step in Developing Game-Specific Measures
Bibliographic record
Abstract
À travers le monde, les paris sportifs représentent la deuxième forme de jeux de hasard et d’argent (JHA) la plus associée aux problèmes de jeu. Les cognitions des parieurs sportifs pourraient contribuer à cette association. Cependant, aucun outil ne semble adapté aux parieurs de loteries sportives, principalement à cause de la composante d’habileté. Cette étude vise (a) à développer l’Inventaire des cognitions à risque — Loteries sportives (ICR-LS) et en déterminer la structure factorielle, (b) à évaluer la validité de convergence de l’ICR-LS avec Gambling Related Cognitions Scale (GRCS), les habitudes de jeu, et la gravité des problèmes de jeu; et (c) à évaluer les liens entre le nombre d’heures mensuelles consacrées à la préparation des paris aux loteries sportives et les habitudes de jeu. Les parieurs sportifs recrutés (N = 272) étaient principalement de sexe masculin (86,5 %), dans la vingtaine (M = 26,7 ans) et issus de la communauté universitaire (88,3 %). Les analyses en composantes principales indiquent que l’instrument possède deux dimensions (Superstitions et Habiletés), une forte cohérence interne (les coefficients alpha > ,85) et une bonne validité convergente. Des associations négligeables, mais statistiquement significatives, ressortent entre l’ICR-LS et le montant annuel dépensé aux loteries, les heures consacrées à la préparation des paris et la gravité des problèmes de jeu. En outre, le temps consacré à la préparation des paris sportifs est modérément corrélé avec le montant dépensé, la fréquence de jeu et la gravité des problèmes de jeu, ce qui incite à y voir, peut-être, un facteur de risque lié aux loteries sportives. Le temps consacré à la préparation des paris sportifs et ses effets sur les différentes sphères de vie mériteraient d’être étudiés davantage, notamment auprès de joueurs problématiques.AbstractAround the world, sports betting is the second type of gambling activity most associated with gambling problems. Thus, sports bettors’ cognitions play an essential role in this association. However, no instrument is specifically designed to assess sports bettors’ cognitions. This study aims (a) to develop the Inventaire des cognitions à risque — Loteries sportives (ICR-LS) and to determine its factor structure, (b) to assess the convergent validity of the ICR-LS with the Gambling Related Cognitions Scale (GRCS), gambling habits, and the severity of gambling problems; and (c) to assess the links between the number of monthly hours spent preparing for sports lottery bets and gambling habits. Participants are sport lottery bettors (N = 272) that are mainly men (86.5%) in their twenties (M = 26.7 years old), and from a university community (88.3%). Principal component analysis results indicate that the instrument is composed of two factors (Superstitions and Abilities), and shows strong internal consistency (coefficients alpha > .85) and good convergent validity. The scale shows statistically significant but negligible associations with the annual amount spent on lotteries, hours spent on the preparation of bets, and gambling problem severity. In addition, time dedicated to bet preparation is moderately associated with the amount spent, gambling frequency, and gambling problem severity, suggesting that sports bettors bet preparation time could be a risk factor in sports betting. Studies should explore further the amount of time dedicated to bet preparation and its effects on different spheres of life, especially for problem gamblers.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".