A Pilot Evaluation of a Tutorial to Teach Clients and Clinicians About Gambling Game Design
Bibliographic record
Abstract
This paper describes the pilot evaluation of an Internet-based intervention, designed to teach counselors and problem gamblers about how electronic gambling machines (EGMs) work. This study evaluated the tutorial using assessment tools, such as rating scales and test of knowledge about EGMs and random chance. The study results are based on a number of samples, including problem gambling counselors ( n = 25) and problem gamblers ( n = 26). The interactive tutorial was positively rated by both clients and counselors. In addition, we found a significant improvement in scores on a content test about EGM games for both clients and counselors. An analysis of the specific items suggests that the effects of the tutorial were mainly on those items that were most directly related to the content of the tutorial and did not always generalize to other items. This tutorial is available for use with clients and for education counselors. The data also suggest that the tutorial is equally effective in group settings and in individual settings. These results are promising and illustrate that the tool can be used to teach counselors and clients about game design. Furthermore, research is needed to evaluate its impact on gambling behavior.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".