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Record W2899084610 · doi:10.4309/jgi.2018.39.7

Responsible Gambling Strategies: Are They Effective Against Problem Gambling Risk in Older Ontarians?

2018· article· en· W2899084610 on OpenAlexaffvenueabout
Éric R. Thériault, Joan E. Norris, Joseph Tindale

Bibliographic record

VenueJournal of Gambling Issues · 2018
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of GuelphWilfrid Laurier UniversityCape Breton University
Fundersnot available
KeywordsPsychologyGambling disorderDemographicsPsychiatryDemographyAddictionSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.434
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes3
Has abstractyes

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