Self-Assessment of Drinking on the Internet--3-, 6- and 12-Month Follow-Ups
Bibliographic record
Abstract
AIM: The aim of this work was to report on the results of a pilot study of a web-based self-assessment service (DHT) for Finnish drinkers (www.paihdelinkki.fi/testaa/juomatapatesti). METHOD: During the 7-month recruitment period in 2004 altogether 22,536 anonymous self-assessments were recorded in the database of this service. The study sample was recruited from the 1598 service users who also participated to a survey evaluating the DHT. Those who consented by providing required baseline data and their e-mail address (n = 343) were sent a message asking them to fill in the follow-up questions 3, 6 and 12 months later. Their self-reported use of alcohol and drinking-related problems served as the main outcome variables in this single-group follow-up study. RESULTS: At 3, 6 and 12 months, 78%, 69% and 61% of the study participants, respectively, responded to the follow-up. The intention-to-treat (ITT) results revealed significant reductions (P < 0.001) in all the outcome measures. The reductions occurred during the first 3 months, after which the changes were non-significant. CONCLUSIONS: The results are in line with previous studies with mostly shorter follow-up periods suggesting that Internet-based self-assessment services can be useful tools in reducing excessive drinking. A randomized controlled trial would, however, increase our certainty about the causes of the observed changes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".