Methodological issues in the evaluation of Internet‐based interventions for problem drinking
Bibliographic record
Abstract
INTRODUCTION AND AIMS: In recent years, there has been an increase in the number of Internet-based interventions (IBI) for alcohol problems and other addictive behaviours. However, it is risky to assume interventions that have been found to work in face-to-face modalities can be translated into IBI that are equally effective. DESIGN AND METHODS: Using selected examples from the published works, this paper will identify some of the special considerations that are relevant to the evaluation of IBI. In addition, methodological issues found in the ongoing development and evaluation of the Check Your Drinking screener (http://www.CheckYourDrinking.net), an IBI for problem drinkers, will be discussed. RESULTS: There have been several randomised control trials with promising results. A primary limitation of much of the research conducted to date is concerns regarding the generalisability of the findings. DISCUSSION AND CONCLUSIONS. Caution should be taken in assuming that the IBI, which have been found to work in tightly controlled efficacy trials, will display similar levels of effectiveness when used in 'naturalistic' settings (i.e. not face-to-face in a research environment). Positive results from studies using a variety of different research designs will advance the potential for IBI, as a new means of helping problem drinkers reduce their alcohol consumption. Because of their accessibility and anonymity, IBI could facilitate a broad provision of treatment services at a population level.
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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 | 0.669 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".