Notice bibliographique
Résumé
Dr Cunningham [1] raises some excellent points regarding this study [2]. He speaks largely to the limitations of the survey data from which the risk curves were generated, rather than the analytical approach taken. He is correct in pointing out that the frequency data on each type of gambling were collected independently. Our composite measure of reported gambling frequency has limitations and is likely to be an underestimate of actual frequency when one considers that individuals can and often do engage in different gambling activities on different days of the month. The missing data on gambling consequences for a large proportion of the original sample is another limitation of the survey over which we had no control. Dr Cunningham is correct that many of the excluded respondents reported gambling in the last year, but self-identified as non-gamblers in the first screening question of the gambling consequences section. These individuals were not administered any additional questions on consequences. The rationale for this decision provided by Statistics Canada is that pilot testing of the CCHS-1.2 (Canadian Community Health Survey, Cycle 1.2—Mental Health and Well-Being) revealed that many of the low-frequency gamblers and individuals who self-identified as being non-gamblers (despite having reported some gambling activity in the preceding questions) strongly objected to being asked the consequences questions. Rather than risk having individuals terminate the interview prematurely, the decision was made to administer the consequences questions only to people who identified as gamblers. It is likely that the majority of the excluded people would report zero or few gambling-related problems. However, this is an assumption we cannot verify. We agree that it limits the generalizability of the results. These limitations speak to the challenge of using survey data for reasons other than its intended purpose. The CCHS-1.2 and other problem gambling prevalence surveys were not developed for the purpose of generating dose–response risk curves. Similarly, population health surveys on alcohol consumption patterns [3] were not developed for the purpose of constructing low-risk drinking guidelines, although data from such surveys were ultimately used for that purpose. We acknowledge the need to cross-validate our findings with other survey data. Our study may also inform the development of future surveys to ensure that accurate data on the dimensions of gambling behavior are collected to compare with risk of gambling-related harm.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».