TREATMENT Twelve-Month Follow-up Results from a Randomized Controlled Trial of a Brief Personalized Feedback Intervention for Problem Drinkers
Bibliographic record
Abstract
Abstract — Aims: To examine the impact of a web-based personalized feedback intervention, the Check Your Drinking (CYD; www. CheckYourDrinking.net) screener at 12-month follow-up. Methods: Respondents (N = 185) were recruited from a general population telephone survey of Ontario, Canadian adults (≥18 years) by asking risky drinkers if they were willing to help develop and evaluate Internet-based interventions for drinkers. Those randomly assigned to the intervention condition were provided with the web address and a unique password to a study-specific copy of the CYD. Respondents assigned to the control condition were sent a written de-scription of the different components of the CYD and asked how useful they thought each of the components might be. Respondents were followed up at 3, 6 and 12months. Results: By the 12-month follow-up, the impact of the intervention previously reported at 3 and 6months of CYD on problem drinkers ’ alcohol consumption was no longer apparent (P> 0.05). Conclusions: Recognizing that many people with alcohol concerns will never seek treatment, recent years have seen an increase in efforts to find ways to take treat-ment to problem drinkers. The CYD is one such intervention that has a demonstrated effect on reducing alcohol consumption in the short term (i.e. 6months). Other more intensive Internet-based interventions or interventions via other modalities may enhance this positive outcome over the short and long term among problem drinkers who would be otherwise unlikely to access treatment for their
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".