The alcohol tracker application: an initial evaluation of user preferences
Bibliographic record
Abstract
BACKGROUND: The prevalence of at-risk drinking and alcohol use disorders is increasing. Advances in technology have resulted in numerous smartphone applications for this disorder. However, there are still concerns about the evidence base of previously developed alcohol applications. OBJECTIVE: The following study aims to illustrate how the authors have made use of innovative methodologies to overcome the issues relating to the accuracy of tracking the amount of alcohol one has consumed; it also aims to determine user perceptions about the innovative tracker and various other features of an alcohol self-management application among a group of individuals from the general population of a developed country (Canada). METHODOLOGY: A native alcohol self-management application was developed. In order to determine user perspectives towards this new innovative application, the authors took advantage and made use of crowdsourcing to acquire user perspectives. RESULTS: Our results showed that smartphone ownership is highest among the age group of 35-44 years (91%) and lowest for those aged between 55 and 64 (58%). Our analysis also showed that 25-34-year-olds and 35-44-year-olds drink more frequently than the other groups. Results suggest that notification and information were the two most useful functions, with psychotherapy expected to be the least useful. Females indicated that notification service was the most useful function, while males preferred the information component. CONCLUSIONS: This study has demonstrated how the authors have made use of innovative technologies to overcome the existing concerns pertaining to the utilisation of the blood alcohol concentration levels as a tracker. In addition, the authors have managed to highlight user preferences with regard to an alcohol application.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".