Assessing harm severity: a Rasch analysis of the gambling harm measure
Bibliographic record
Abstract
The Gambling Harm Measure (GHM) is a 16-item self-report instrument used to measures gambling-related harm (GRH) across its six principal domains. Unlike other GRH measures, the GHM uses multiple items to assess harm along a gradient of within each of its principle domain (excluding illegal acts). The objective of this study was to evaluate this severity gradient using Rasch analysis. Data from 2,941 respondents were obtained from three independent surveys, two conducted in the American state of Massachusetts and one conducted nationally across Canada. Item thresholds varied within each domain, providing evidence that the GHM can capture harm across a gradient of severity. There was evidence of differential item functioning (DIF), with younger adults and those participating in more harmful gambling formats (e.g. electronic gambling machines, casino table games) being more likely to endorse several items on the GHM. Finally, we identified three discrete categories of GHM scores: mild harm included individuals endorsing 1–6 items; high harm included individuals endorsing 7–13 items; and severe harm included individuals endorsing 14 or more items. This study provides support for the GHM as a valid measure of GRH that can capture both the range and severity of harm experienced by individuals who gamble.
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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 | 0.001 | 0.002 |
| 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".