Information technology and addiction science: promises and challenges
Bibliographic record
Abstract
BACKGROUND: Information technology can be used to advance addiction science and clinical practice. MAIN BODY: This special issue, "Information technology (IT) interventions to advance treatment for opioid and other addictions" presents studies that expand our understanding of IT intervention efficacy, patients' perspectives, and how IT can be used to improve substance use health care and research. This editorial introduces the topics addressed in the special issue and focuses on some of the challenges that the field is currently facing, such as attrition and treatment retention, transferability of intervention paradigms, and the challenge to keep pace with rapidly changing technologies. CONCLUSIONS: Increasing treatment reach is particularly crucial in the addiction field. IT empowers researchers and clinicians to reach large portions of the population who might not otherwise access standard treatment modalities, because of geographical limitations, logistical constraints, stigma, or other reasons. The use of information technology may help reduce the substance use treatment gap and contribute to public health efforts to diminish the impact of substance use and other addictive behaviors on population health.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".