E-POP profile user – a mobile application supporting the reduction of alcohol consumption. A pilot study
Bibliographic record
Abstract
Introduction: Mobile phone applications expand the range of services for those with alcohol problems, especially during the SARS-CoV-2 pandemic when traditional treatment is not so readily available.At the request of the State Streszczenie Wprowadzenie: Aplikacje telefoniczne poszerzają ofertę pomocy dla osób z problemem alkoholowym, zwłaszcza w okresie utrudnionego dostępu do tradycyjnego systemu lecznictwa, jakim jest pandemia SARS-CoV-2.Na zlecenie Państwowej ID ID ID ID IDof women, and suspected dependence was reported by 27.8% of men and 17.3% of women.Twenty six percent of persons registered and entered the therapeutic stage.This group was dominated by people of 30-39 (35.2%) and 40-49 (29.8%) years of age.Discussion: There is a small number of published studies evaluating alcohol reduction mobile phone applications.The obtained E-POP application user profile is consistent with the characteristics of users of similar applications around the world. Conclusions:The users are most often aged between 30 to 49 and over 70% register and decide to start therapeutic work.About 30% met the criteria for mild or moderate alcohol use disorder severity.One in three who participate in a treatment programme are most likely to reduced drinking.
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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 | 0.006 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".