Psychometric Properties of the Swedish Version of the Reasons for Gambling Questionnaire (RGQ)
Bibliographic record
Abstract
The Reasons for Gambling Questionnaire (RGQ) is a self-report instrument containing 15 items to assess individual motives or reasons to gamble. This study presents psychometric data on the Swedish version of the instrument with a focus on factor structure. A Swedish sample (N = 19,530) was screened for risk gambling with the Lie/Bet questionnaire, the effective study sample (n = 237) consisting of respondents with a positive answer on this questionnaire who agreed to participate in an additional postal questionnaire and had no missing items on the RGQ. The originally proposed subscales of the instrument fit the data poorly and a slightly different five-factor solution was suggested. We conclude that the RGQ needs further revision and that the dimensionality of gambling motives is a question that deserves further attention. RésuméLe questionnaire sur les raisons du jeu (RGQ) est un instrument d’auto-évaluation proposant quinze points d’évaluation des motivations ou raisons de jouer. Cette étude présente quelques données psychométriques sur la version suédoise du questionnaire en mettant l’accent sur la structure factorielle. Un échantillon suédois (n = 19 530) a été soumis à l’examen pour déterminer le jeu à risque à l’aide du questionnaire Lie-Bet, et l’échantillon efficace (N = 237) était formé de répondants ayant obtenu un résultat positif au questionnaire Lie-Bet, ayant accepté de participer à un questionnaire supplémentaire par la poste et n’ayant omis aucun élément dans le RGQ. La sous-échelle du questionnaire proposée à l’origine a mal adapté les données, et on a proposé une solution à cinq facteurs légèrement différente. En conclusion, le RGQ doit être révisé, et la dimensionnalité des raisons du jeu est une question qui mérite une attention accrue.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".