“The post-COVID era”: challenges in the treatment of substance use disorder (SUD) after the pandemic
Bibliographic record
Abstract
BACKGROUND: Citizens affected by substance use disorders are high-risk populations for both SARS-CoV-2 infection and COVID-19-related mortality. Relevant vulnerabilities to COVID-19 in people who suffer substance use disorders are described in previous communications. The COVID-19 pandemic offers a unique opportunity to reshape and update addiction treatment networks. MAIN BODY: Renewed treatment systems should be based on these seven pillars: (1) telemedicine and digital solutions, (2) hospitalization at home, (3) consultation-liaison psychiatric and addiction services, (4) harm-reduction facilities, (5) person-centered care, (6) promote paid work to improve quality of life in people with substance use disorders, and (7) integrated addiction care. The three "best buys" of the World Health Organization (reduce availability, increase prices, and a ban on advertising) are still valid. Additionally, new strategies must be implemented to systematically deal with (a) fake news concerning legal and illegal drugs and (b) controversial scientific information. CONCLUSION: The heroin pandemic four decades ago was the last time that addiction treatment systems were updated in many western countries. A revised and modernized addiction treatment network must include improved access to care, facilitated where appropriate by technology; more integrated care with addiction specialists supporting non-specialists; and reducing the stigma experienced by people with SUDs.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".