Development of the Saini-Hodgins Addiction Risk Potential of Games (SHARP-G) Scale: An International Delphi study
Why this work is in the frame
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Bibliographic record
Abstract
Background and Objectives: As the gaming industry experiences exponential growth, concerns about gaming disorder (GD) also grow. It is crucial to understand the structural features of games that can interact with individual characteristics of gamers to promote GD. This research consolidates the views of an international body of panelists to create an assessment tool for gauging the addictive potential of distinct games. Methods: Utilizing the iterative and structured Delphi method, an international panel of researchers, clinicians, and people with lived experience were recruited to offer a multifaceted viewpoint on the addictive risk associated with specific structural elements in games. Two rounds of surveys facilitated consensus. Results: The panel initially included 40 members-ten from research, eight from clinical settings, and 22 with lived experiences. The second round included 27 panelists-seven from research, eight from clinical settings, and 11 with lived experiences. The study identified 25 structural features that contribute to potentially addictive gaming patterns. Discussion and Conclusions: Consensus was found for 25 features, which were distilled into a 23-item evaluation tool. The Saini-Hodgins Addiction Risk Potential of Games Scale (SHARP-G) consists of five overarching categories: 'Social,' 'Gambling-Like Features,' 'Personal Investment,' 'Accessibility,' and 'World Design.' SHARP-G yields a total score indicating level of addiction risk. A case study applying the scale to three games of differing perceived risk levels demonstrated that that score corresponded to game risk as expected. While the SHARP-G scale requires further validation, it provides significant promise for evaluating gaming experiences and products.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it