Internet‐based interventions for problem drinkers: From efficacy trials to implementation
Bibliographic record
Abstract
AIMS: Internet-based interventions (IBIs) for problem drinkers have been in existence for over a decade. In that time, IBIs have increased in sophistication and there is the beginning of a solid research base suggesting their efficacy. A growing number of problem drinkers are using IBIs and attempts have been made to explore how IBIs can be integrated within primary care and other health-care settings. This symposium provided an overview of IBIs for problem drinkers and highlighted some of the important issues in their development and implementation. RATIONALE: IBIs appear to be at a 'cusp' as technology and intervention practices are merged together in an attempt to provide better health care for problem drinkers. The timing of the 2009 International Network on Brief Interventions for Alcohol Problems Conference was ideal for a presentation and discussion of the role that IBIs play now that IBIs have started to shift into the mainstream of services for problem drinkers. SUMMARY: The presentations in this symposium covered the 'bench to bedside' aspects of the development and evaluation of IBIs. They included a systematic review of the research to-date in this field, a report on the results from a just completed randomised controlled trial, a report on an effectiveness trial of implementing IBIs in multiple university settings and a consideration of the cost-effectiveness of IBIs.[Cunningham JA, Khadjesari Z, Bewick BM, Riper H. Internet-based interventions for problem drinkers: From efficacy trials to implementation.
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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 | 0.125 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".