Responsible Gambling Strategies: Are They Effective Against Problem Gambling Risk in Older Ontarians?
Bibliographic record
Abstract
Despite the limited amount of research on gambling in older adults (55+ years), they are often encouraged to use responsible gambling strategies to ensure that it remains a “low-risk” activity. However, the effectiveness of these strategies has not been examined in this population. The purpose of this study was threefold: to document the types of responsible gambling strategies used by older Ontario residents, to examine how these strategies relate to problem gambling risk, and to assess whether there are differences in the use of responsible gambling strategies between those who are and are not at risk of problem gambling. We examined the data of 673 older adults (M = 68.7, SD = 7.6) from three different studies that used the same measurement instruments to assess demographics, problem gambling risk, and responsible gambling strategies (Norris & Tindale, 2006; Thériault, 2015; Tindale & Norris, 2015). We failed to find any evidence that the use of responsible gambling strategies was related to the risk of problem gambling in older adults (as measured by the Problem Gambling Severity Index of the Canadian Problem Gambling Index and the Windsor Screen). The respondents who used these strategies did not have a lower problem gambling risk than did the respondents who did not use the strategies. Further, the number of strategies used did not vary between problem gambling risk categories. These results raise questions about the utility of strategies used for responsible gambling.RésuméMalgré le nombre restreint de recherches effectuées sur le jeu chez les personnes âgées de 55 ans et plus, on constate que ces personnes sont souvent invitées à recourir à des stratégies de jeu responsable pour s’assurer que cette activité demeure « à faible risque ». L’efficacité de ces stratégies n’a cependant pas été examinée dans cette population. La raison d’être de cette étude est triple : répertorier les types de stratégies de jeu responsable utilisées par les personnes âgées en Ontario, examiner comment ces stratégies sont liées au risque de jeu compulsif, et évaluer s’il existe des différences entre les personnes à risque de jouer de manière compulsive et celles qui ne le sont pas dans l’utilisation de stratégies de jeu responsable. Au total, 673 personnes âgées (moyenne = 68,7, ÉT = 7,6) ont été recrutées dans trois études différentes recourant aux mêmes instruments de mesure; les mesures évaluaient les données démographiques, le risque de jeu problématique et les stratégies de jeu responsable (Norris et Tindale, 2006; Tindale et Norris, 2015; Thériault, 2015). L’étude n’a pas permis de prouver que l’utilisation de stratégies de jeu responsable était liée au risque de jeu excessif chez les personnes âgées (tel que mesuré par l’Indice canadien du jeu problématique, l’Indice de gravité du jeu problématique et le dépistage de Windsor). Les répondants qui ont utilisé ces stratégies n’affichaient pas un risque de jeu problématique inférieur à ceux qui ne les utilisaient pas. Enfin, le nombre de stratégies utilisées n’a pas varié entre les catégories de risque de jeu problématique. Ces résultats soulèvent des questions quant à l’utilité des stratégies employées pour assurer le jeu responsable.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".