Evaluating the Reliability and Validity of the Short Gambling Harm Screen: Are Binary Scales worse than Likert Scales at capturing Gambling Harm?
Bibliographic record
Abstract
Gambling-related harm has become a key metric for measuring the adverse consequences of gambling on a population level. Yet, despite this renewed understanding in contemporary research, little exploration has been conducted to evaluate which instrument is best suited to capture the harmful consequences of gambling. This study was designed with the aim of determining whether Likert scales were better suited to capture gambling harm than binary scales. We hypothesized that the Short Gambling Harm Screen (SGHS), initially scored using a binary scale, would perform similarly to the alternate form that was Likertized for the purpose of this study. A corresponding comparison in the reverse direction was executed for the Problem Gambling Severity Index. The SGHS’s performance was assessed via a repeated-measures design in combination with three other measures of validity administered at the conclusion of the survey. In the end, we found that changing the scoring format (i.e., from binary to Likert) had negligible impact on the SGHS’s psychometric performance. We conclude that the original scoring method of the SGHS is not only appropriate but also no less suitable than Likert scales in measuring gambling harm.RésuméLes dommages liés au jeu sont devenus une mesure clé pour évaluer les conséquences néfastes du jeu à l’échelle de la population. Pourtant, malgré cette compréhension renouvelée dans la recherche contemporaine, on effectue très peu d’exploration pour évaluer quel instrument est le mieux adapté pour comprendre les conséquences néfastes du jeu. Cette étude a été conçue dans le but de déterminer si les échelles de Likert étaient mieux adaptées que les échelles binaires pour saisir les dommages liés au jeu. Nous avons émis l’hypothèse que le dépistage rapide du jeu problématique (Short Gambling Harm Screen ou SGHS), initialement évalué à l’aide d’une échelle binaire, ne fonctionnera pas différemment de la forme de Likert alternative qui a été créée aux fins de cette étude. Une comparaison correspondante dans la direction inverse a été effectuée pour l'indice de gravité du jeu excessif (PGSI). Les performances du SGHS ont été évaluées par un plan de mesures répétées, combinés à trois autres mesures de validité administrées à la fin du sondage. En fin de compte, nous avons constaté que le changement du format de pointage (c.-à-d. du binaire au Likert) avait un impact négligeable sur le rendement psychométrique du SGHS. Nous concluons que la méthode de pointage originale du SGHS est non seulement appropriée, mais également non moins appropriée que les échelles de Likert pour évaluer les dommages liés au jeu.
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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 | 0.045 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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; 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".