Web-based Interventions for Substance Use Disorders and Mental Health: Preliminary findings from a Scoping Review.
Bibliographic record
Abstract
This scoping review evaluated the efficacy and potential of web-based interventions for substance use disorders and mental health conditions. The studies comprise randomized controlled trials, pilot trials, and effectiveness trials. Web-based interventions consistently demonstrated significant reductions in substance use, improvements in mental health outcomes (e.g., PTSD, depression, anxiety), and enhancements in emotion regulation, help-seeking, and quality of life. Several studies found web-based interventions to be non-inferior or superior to traditional face-to-face treatments. Despite limitations in the current evidence base, such as methodological issues and lack of long-term follow-up, the findings highlight the promise of web-based interventions in expanding access to evidence-based care, particularly for underserved populations. Future research should focus on refining interventions, exploring novel technologies, and evaluating long-term effectiveness and cost-effectiveness. The integration of web-based interventions into healthcare systems has the potential to significantly impact public health by increasing treatment accessibility and improving outcomes for individuals with substance use disorders and mental health conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".