The Ordering of Gambling Severity and Harm Scales: A Cautionary Tale
Bibliographic record
Abstract
Question-order effects are known to occur in surveys, particularly those that measure subjective experiences. The presence of context effects will impact the comparability of results if questions have not been presented in a consistent manner. In this study, we examined the influence of question order on how people responded to two gambling scales in the Australian Capital Territory Gambling Prevalence Survey: The Problem Gambling Severity Index and the Short Gambling Harm Screen. The application of these scales in gambling surveys is continuing to grow, the results being compared across time and between jurisdictions, countries, and populations. Here we outline a survey experiment that randomized the question ordering of these two scales. The results show that question-order effects are present for these scales, demonstrating that results from them may not be comparable across jurisdictions if the scales have not been presented consistently across surveys. These findings highlight the importance of testing for the presence of question-order effects, particularly for those scales that measure subjective experiences, and correcting for such effects where they exist by randomizing scale order.
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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 | 0.408 | 0.665 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.007 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".