Crushing Virtual Cigarettes Reduces Tobacco Addiction and Treatment Discontinuation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pilot studies revealed promising results regarding crushing virtual cigarettes to reduce tobacco addiction. In this study, 91 regular smokers were randomly assigned to two treatment conditions that differ only by the action performed in the virtual environment: crushing virtual cigarettes or grasping virtual balls. All participants also received minimal psychosocial support from nurses during each of 12 visits to the clinic. An affordable virtual reality system was used (eMagin HMD) with a virtual environment created by modifying a 3D game. Results revealed that crushing virtual cigarettes during 4 weekly sessions led to a statistically significant reduction in nicotine addiction (assessed with the Fagerström test), abstinence rate (confirmed with exhaled carbon monoxide), and drop-out rate from the 12-week psychosocial minimal-support treatment program. Increased retention in the program is discussed as a potential explanation for treatment success, and hypotheses are raised about self-efficacy, motivation, and learning.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 it