An Ethicolegal Analysis of Involuntary Treatment for Opioid Use Disorders
Bibliographic record
Abstract
Supply-side interventions such as prescription drug monitoring programs, "pill mill" laws, and dispensing limits have done little to quell the burgeoning opioid crisis. An increasingly popular demand-side alternative to these measures - now adopted by 38 jurisdictions in the USA and 7 provinces in Canada - is court-mandated involuntary commitment and treatment. In Massachusetts, for example, Part I, Chapter 123, Section 35 of the state's General Laws allows physicians, spouses, relatives, and police officers to petition a court to involuntarily commit and treat a person whose alcohol or drug abuse poses a likelihood of serious harm. This paper explores the ethical underpinnings of this law as a case study for others. First, we highlight the procedural and substantive standards of Section 35 and evaluate the application of the law in practice, including the frequency with which it has been invoked and outcomes. We then use this background to inform an ethical critique of the law. Specifically, we argue that the infringement of autonomy and privacy associated with involuntary intervention under Section 35 is not currently justified on the grounds of a lack of evidenced benefits and a risk of significant of harm. Further ethical concerns also arise from a lack of standard of care provided under the Section 35 pathway. Based on this analysis, we advance four recommendations for change to mitigate these ethical shortcomings.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".