Preliminary Investigation of a Videogame Prototype for Cigarette and Marijuana Prevention in Adolescents
Bibliographic record
Abstract
BACKGROUND: Videogames are becoming increasingly popular to deliver health interventions; however, their role in the primary prevention of cigarette and marijuana use has not yet been evaluated. The purpose of this study was to conduct a preliminary test of the efficacy of a role-playing videogame prototype, smokeSCREEN, aimed at developing knowledge and behavioral skills associated with primary prevention of cigarette and marijuana use. The authors also explored participants' gameplay experience. METHODS: = 11.56, SD = 0.77) who had never tried cigarettes or marijuana. Participants played four 1-hour gameplay sessions over a 2-week period. Assessments of knowledge, self-efficacy, attitudes, perceived norms, and intentions related to cigarette and marijuana prevention were collected at baseline and 2-week and 12-week follow-ups. Ratings of gameplay experience were collected after the 2 weeks of gameplay. One-way repeated-measures analyses of variance (ANOVAs) were conducted. RESULTS: Findings are (1) improvements in knowledge for both cigarette (Wilks' λ = 0.62, F(2, 23) = 7.21, P = .004) and marijuana (Wilks' λ = 0.67, F(2, 23) = 5.75, P = .009) use from pre- to post-gameplay that were characterized by large effects; and (2) nonsignificant trends in the expected direction emerged for changes in self-efficacy and perceived norms related to both cigarettes and marijuana that were characterized by medium-large effects. Overall, the players provided positive reports of their experience with the smokeSCREEN videogame prototype. CONCLUSIONS: These findings provide preliminary evidence that a videogame has the potential to influence key cognitive and motivational variables and can be an engaging means to deliver a cigarette and marijuana prevention intervention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".