Notice bibliographique
Résumé
Dear Editor: I read with enthusiasm the triad of articles dedicated to pathological gambling in the August 2004 issue of the CJP (1-3), but when finished, I felt somehow disappointed. The persistent avoidance of even mentioning 12-step-based models of addiction as alternatives to understanding pathological gambling in fact greatly limits the 3 papers. The editorial by Dr Ladouceur attempts to explain problem gambling in terms of a pure cognitive model (1) wherein pathological gambling stems from the sufferer's inability to understand the independent randomness of chance events, confirmed by the fact that most individuals will hold nonscientific and false beliefs if exposed to gambling. The typical example offered is the situation in which, after the tossing of a coin has resulted in several consecutive tails, most people will believe that chances for heads have increased with the next toss. However, this phenomenon does not explain pathological gambling; it just explains how most people think. It is almost like saying that people gamble because they have 2 feet! As Dr Ladouceur admits, two-thirds of adults gamble, and most people find it difficult to understand randomness, preferring to interpret reality within a deterministic framework. Dr Ladouceur suggests that these false beliefs are more strongly held among problem gamblers than among the general population, and for that reason, they cannot stop gambling, even in the face of loss and self-destruction. This line of reasoning equates to using our knowledge of why the general population drinks moderately to explain why people become dependent on alcohol; it is obviously fallacious. If Dr Ladouceur considered instead a cross-addiction model of pathological gambling, he would not find it difficult to observe the similarities between problem gamblers' insistence on gaming, despite financial ruin, and the persistent addictive behaviour of patients dependent on alcohol or opiates, despite the tragic consequences. Although Dr Shaffer and others highlight the high levels of comorbidity between chemical addictions and pathological gambling (2), they never consider that this comorbidity could be the manifestation of a common pathology. If the authors had included in the comorbidity with problem gambling not only chemical addictions but also such other addictive behaviours as binge eating and sex addiction, they would have found a concordance close to 100%. Looking at addiction, including pathological gambling, as a unitary problem, would also have helped the authors to understand the life trajectories described in their article. The fact that pathological gamblers are not constantly involved in gambling is not surprising, according to a cross-addiction model. All clinicians involved in addiction treatment observe their patients switching among different addictive behaviours during their lifetime. The addiction is lifelong; the way to express it changes. I am confident that, if the authors were to observe patients who seem to recover from their gambling problem longitudinally, they would realize that, in reality, most have just transferred their addiction to other addictive behaviours such as alcohol abuse, substance abuse, smoking, overeating, and pathological sexual promiscuity. As a last remark, it is unacceptable to review treatments available for pathological gambling (3) without mentioning 12-step fellowships and treatment centres that follow this philosophy, given that most patients who try to fight addiction on both sides of the Atlantic are helped by this model. References 1. Ladouceur R. Gambling: the hidden addiction. Can J Psychiatry 2004;49:1-3. 2. Shaffer HJ, LaBrie RA, LaPlante DA, Nelson SA, Stanton MV. The road less travelled: moving from distribution to determinants in the study of gambling epidemiology. Can J Psychiatry 2004;49:504-16. 3. Toneatto T, Millar G. Assessing and Treating problem gambling: empirical status and promising trends. …
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,014 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».