A Brief Outline of the Use of New Technologies for Treating Substance Use Disorders in the European Union
Bibliographic record
Abstract
BACKGROUND: Clinicians in the field of drug addiction have started to exploit the growth of Technology-Based Interventions (TBIs). However, there is little information on how health personnel evaluate them. METHODS: Semi-structured interviews were conducted among 20 European experts. RESULTS: All of the interviewees recognised TBIs as a valuable tool to improve the management of substance-use disorders (SUDs). Most interviewees indicated that combining both traditional face-to-face therapist-patient clinic appointment with TBIs is probably the most effective method. Most interviewees agree that TBIs are valuable tools to overcome both physical and social barriers, and hence significantly facilitate the access to treatment. Poor infrastructure and lack of digital literacy are recognised as major barriers to the diffusion of these tools. CONCLUSIONS: The application of various forms of technology in SUD treatment is an interesting development for the European Union. Technical and non-technical barriers exist and impede their full exploitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".