Thinking of Changing your Alcohol and Tobacco Use? There's an App for That!
Bibliographic record
Abstract
Self-change is a well-documented phenomenon in the addiction field. Because many individuals with alcohol problems do not seek treatment due to stigma, cost, and their problems are not severe, developing interventions that can be implemented outside of clinical settings is important. In this regard, while electronic/online interventions have become popular, evidence-based interventions for mobile phones are scarce. Mobile phone applications (apps) have portability and can be accessed and used by a wide and diverse range of people. Despite these advantages, evidence-based apps to treat alcohol problems do not exist. The need to reach the "unreachable" resulted in the Guided Self-Change (GSC) treatment model being evaluated as a mail intervention in two randomized clinical trials (RCTs) in Canada and the US. The mail intervention consisting of personalized feedback materials was very cost-effective, reached large numbers of individuals who had never sought treatment before, and helped these individuals reduce their drinking. The advent of phone apps provides an opportunity to reach an even greater number of individuals who otherwise may not seek services. This study will develop and evaluate an iPhone app based on the GSC mail intervention, after which the app will be tested for usability. The app will include smoking self-monitoring logs because research shows that heavy drinkers are more likely to be smokers, and because a major factor associated with drinking relapse is smoking. A second phase will involve a RCT using a two-group design comparing the app with its evidence-based counterpart, the mail intervention, using a 3-month follow-up with participants recruited via advertisements. The clinical and public health implications of this project are considerable. Extending the evidence-based mail intervention to an evidence-based app would provide privacy, immediate personalized feedback about alcohol and tobacco use and its risks, and most importantly, 24-hour access to a therapy-based 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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".