[Commentary] WHAT WE SEE DEPENDS MAINLY ON WHAT WE LOOK FOR (JOHN LUBBOCK, BRITISH ANTHROPOLOGIST, 1834–1913)
Notice bibliographique
Résumé
The gambling and gambling disorders field has been influenced strongly by the more extensive and more mature body of research reported in the substance use and abuse area. In fact, a number of top substance abuse researchers have shifted into gambling research as gambling disorders have become an increasing concern world-wide. Stewart and colleagues [1] are extending to gambling work that which they and others have conducted on drinking motives. The results of this instrument development study suggest that there are a number of similarities between gambling and drinking motives. Three factors were uncovered that predicted differentially gambling frequency and gambling problems, mainly consistent with the alcohol literature. With alcohol, this research line has led to practical advancements in tailoring treatments to motives [2,3], and the hope is that similar advancements will be made for gambling. Although the impact of substance abuse research on the gambling field has been mainly positive, the potential weakness is that substance abuse researchers will find only what they look for. Stewart and colleagues recognize this potential limitation when they note that there are possibly other motives unique to gambling, not measured by their instrument. I remember very clearly, at my first gambling conference in the early 1990s when I was shifting over to gambling from substance abuse, Garry Smith, a gambling research pioneer (now a close colleague) describing the ‘4 E's of gambling’—entertainment, excitement, escape and to win money (economic gain)’. The first three of these motives correspond roughly to Stewart's gambling motives. Concerning the fourth reason, however, the financial motive is specific to gambling and, in fact, is a very frequently reported reason for gambling. Garry Smith's mnemonic summarized findings from a set of ‘reasons for gambling’ questions that have been included in many of the general population gambling prevalence surveys that have been conducted around the world (e.g. [4–6]). A little investigation revealed that the origin of these questions came from Rachel Volberg and Max Abbott, who included an open-ended question about reasons for gambling in a New Zealand general population survey of gambling (R. Volberg, personal communication, 10 April 2008). Responses were coded into categories with the following results: 57% mentioned winning money as the main reason they gambled; 33% said that they gambled for fun or entertainment, took pleasure in gambling or saw it as fun or recreation; 19% said that they gambled to support worthy causes; 15% said they gambled to socialize; and 15% said they gambled for the excitement or challenge. In numerous subsequent surveys these questions have been asked in closed format, with broadly similar results over time and geography. Most recently a 2006 survey of Californians showed that of weekly gamblers, 75.0% reported entertainment to be somewhat or very important; 66.0% winning money; 48.1% excitement; and 41.3% socializing [6]. In this survey and others [5,6], however, pathological and problem gamblers were more likely to endorse the winning money motive than non-problem gamblers. The importance of money as a motivation was also revealed in some of our research when pathological gamblers described their reasons for relapsing when attempting to quit gambling—optimism about winning and the need to win money were the two most frequently reported relapse precipitants [7]. Using gambling as a strategy to win money is, of course, unrealistic given the clear house advantage in typical gambling situations. This paradox has been identified as a major erroneous cognition that is the focus of cognitive treatment for gambling problems [8]. It may be that the motive is actually the possibility of winning money versus an expectation of actually winning. Survey items such as the ‘reasons for gambling’ options are designed for use in large samples, and scales with stronger psychometric qualities are required for more detailed study with smaller clinical groups. While Stewart and colleagues provide a validated scale to measure some aspects of motives for gambling, other important aspects of this fascinating disorder are also deserving of our attention.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».