Impact of survey description, administration format, and exclusionary criteria on population prevalence rates of problem gambling
Bibliographic record
Abstract
The present study investigated the impact of survey administration format, survey description and gambling behaviour thresholds on obtained population prevalence rates of problem gambling. A total of 3028 adults were surveyed about their gambling behaviour, with half of these surveys administered face-to-face and half over the telephone, and half of the surveys being described as a ‘gambling survey’ and half as a ‘health and recreation’ survey. Population prevalence rates of problem gambling using the CPGI were 133% higher in ‘gambling’ vs ‘health and recreation’ surveys and 55% higher in face-to-face administration compared to telephone administration. If people with less than Can$300 in annual gambling expenditures are not asked questions about problem gambling, then the obtained problem gambling prevalence rate is 42% lower. When all of these elements are aligned they result in markedly different problem gambling prevalence rates (4.1% vs 0.8%). The mechanisms for these effects and recommended procedures for future prevalence studies are discussed.
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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 | 0.199 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".