Validation of the gambling harm measure across three independent samples
Bibliographic record
Abstract
Gambling-related harm (GRH) has become an increasingly popular topic in gambling research. However, several issues concern existing measures of gambling-related harm. The objective of this study was to validate a new measure of GRH, the Gambling Harm Measure (GHM). The GHM is a 16-item instrument that assesses varying levels of harm across six different life domains: financial, psychological, relationship, physical, work/study, and legal. A secondary analysis of data from three independent samples (n = 9,913) was conducted to determine the GHM’s factor structure and its association with alternative measures of gambling-related harm and gambling intensity. Confirmatory factor analyses provided the strongest support for a unidimensional factor structure, indicating that the GHM is best scored as the sum of items endorsed. This factor structure was invariant to differences between samples, age groups, and sexes. Correlational analyses revealed stronger associations between total score on the GHM and alternative measures of GRH compared to measures of other substance-related and behavioral addictions. Finally, generalized linear mixed modeling demonstrated a positive effect of gambling intensity on GHM scores. This study provides support for the use of the GHM as a valid, unidimensional measure of gambling-related harm.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".