Bibliographic record
Abstract
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. …
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".