Bibliographic record
Abstract
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.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.036 | 0.059 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".