A Mobile App to Provide Evidence-Based Information About Crystal Methamphetamine (Ice) to the Community (Cracks in the Ice): Co-Design and Beta Testing
Bibliographic record
Abstract
BACKGROUND: Despite evidence of increasing harms and community concern related to the drug crystal methamphetamine ("ice"), there is a lack of easily accessible, evidence-based information for community members affected by its use, and to date, no evidence-based mobile apps have specifically focused on crystal methamphetamine. OBJECTIVE: This study aims to describe the co-design and beta testing of a mobile app to provide evidence-based, up-to-date information about crystal methamphetamine to the general community. METHODS: A mobile app about crystal methamphetamine was developed in 2017. The development process involved multiple stakeholders (n=12), including technology and drug and alcohol experts, researchers, app developers, a consumer expert with lived experience, and community members. Beta testing was conducted with Australian general community members (n=34), largely recruited by the Web through Facebook advertising. Participants were invited to use a beta version of the app and provide feedback about the content, visual appeal, usability, engagement, features, and functions. In addition, participants were asked about their perceptions of the app's influence on awareness, understanding, and help-seeking behavior related to crystal methamphetamine, and about their knowledge about crystal methamphetamine before and after using the app. RESULTS: The vast majority of participants reported the app was likely to increase awareness and understanding and encourage help-seeking. The app received positive ratings overall and was well received. Specifically, participants responded positively to the high-quality information provided, usability, and visual appeal. Areas suggested for improvement included reducing the amount of text, increasing engagement, removing a profile picture, and improving navigation through the addition of a "back" button. Suggested improvements were incorporated prior to the app's public release. App use was associated with an increase in perceived knowledge about crystal methamphetamine; however, this result was not statistically significant. CONCLUSIONS: The Cracks in the Ice mobile app provides evidence-based information about the drug crystal methamphetamine for the general community. The app is regularly updated, available via the Web and offline, and was developed in collaboration with experts and end users. Initial results indicate that it is easy to use and acceptable to the target group.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".