In substance use treatment, are treatment goals harming or helping?
Bibliographic record
Abstract
The COVID-19 pandemic has driven the adoption of harm reduction strategies in substance use disorder treatment, shifting focus from abstinence to addressing substance-related harm in a person-centered framework. Substance use disorder is a chronic mental health condition with biological and genetic bases that is also protected by the Americans with Disabilities Act. Literature and treatment practices, however, continue to emphasize abstinence approaches over harm reduction. This creates a disconnect between evolving treatment approaches and outdated outcomes. This paper examines the potential harm of adhering to outdated metrics and argues for a reevaluation of treatment outcomes across professions. By highlighting how traditional measures can overlook progress and inflict harm, we advocate for adopting holistic assessments and outcome measures. Embracing harm reduction principles and revising outcome measures can better support recovery, align with person-centered care, and save lives without putting tradition before the true needs of the individual people in recovery.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".