Bibliographic record
Abstract
Rodgers et al.[1] argue cogently for the need to expand gambling research along a number of dimensions. In many ways, the gambling literature is 20–30 years behind the alcohol literature. By applying lessons learned from alcohol research perhaps the gambling field can catch up, but the gambling field faces some unique challenges as well. Rodgers and colleagues [1] suggest the need for further study of how gambling participation is related to future adverse outcomes. They note the need for longitudinal assessments in other areas as well, such as examining predictors of gambling participation and natural recovery. While such relationships are certainly important to evaluate and longitudinal perspectives are valuable, it is at least equally, if not more, imperative to start from a strong cross-sectional viewpoint. As noted in the paper [1], few data are available to address how measures of recent gambling participation (days, time, dollars) relate to concurrent measures of gambling harm. While more gambling—no matter how it is assessed—is probably correlated with increased harm, it may not be a linear relationship. Furthermore, associations may differ based on individuals' life circumstances (e.g. elderly gamblers may wager more frequently without experiencing the same degree of harm as middle-aged employed people), financial standing (e.g. wealthier individuals may gamble more often and spend more—and even greater proportions of their incomes—without experiencing similar degrees of harm as less financially well-off individuals) and type of gambling [2] (e.g. betting daily on scratch tickets may incur less harm than monthly casino or sports gambling). Much more detailed information is needed about gambling participation measures, how they vary across individuals and how they are associated with problems. On a related note, a better understanding of gambling-related harm itself is also needed. Harm can be assessed via diagnostic symptom counts (or diagnoses) or global indices of psychological distress or quality of life. No consensus exists on how best to define gambling-related harm. Some studies consider harmful gambling to be that which exceeds a certain quantity or frequency level, and others label it as meeting one to two, three to four or five or more diagnostic criteria. Different instruments are used, including the South Oaks Gambling Screen, DSM-based measures (which also differ from one another), and the Canadian Problem Gambling Index. Few empirical data exist to recommend any one measure over another, but evaluating associations between gambling participation and problems requires psychometrically sound instruments assessing both constructs. The difficulties that underlie even cross-sectional studies of gambling are forbidable. While lessons learned from the alcohol field should be considered in gambling research, there are some notable differences between the disorders rendering the study of gambling, especially from a longitudinal perspective, even more challenging. Pathological gambling has a low prevalence rate in the general population [3–5]. Thus, conducting epidemiological studies requires very large sample sizes to identify sufficient numbers of individuals who meet diagnostic criteria. Studies can be performed in high-risk populations (e.g. adolescents, college students, low socio-economic groups, disabled, psychiatric populations and substance abusers), but data gathered from these groups may not be applicable to the general population. Finally, treatment-seeking pathological gamblers are at the high end of the problem severity continuum, but again, assessment of their levels of gambling participation and harm is unlikely to be representative of the general population, or even among the majority of pathological gamblers, most of whom do not seek or receive treatment services [4,6,7]. In addition, gambling research, at least in the United States, is at a strong disadvantage relative to substance abuse research. The National Institutes of Health (NIH) funds over 85% of the world's research on substance use disorders. In contrast, pathological gambling is without a home in the NIH, as no branch has claimed this ‘orphan’ disorder. Without inclusion in the DSM-IV, research on its sub-diagnostic threshold condition is even less likely to be funded. In summary, while I agree with all of Rodgers et al.'s [1] points regarding important areas for future study, some first steps should precede more complex study designs. A better understanding of cross-sectional data regarding gambling diagnoses, classifications, frequencies, intensities and types, along with assessment of harm using psychometrically sound instruments that assess a range of potential problems, should perhaps be the initial approach. None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; both teacher heads agree on what is shown here.
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".