Social control and coercion in addiction treatment: towards evidence‐based policy and practice
Bibliographic record
Abstract
BACKGROUND: Social pressures are often an integral part of the process of seeking addiction treatment. However, scientists have not developed conclusive evidence on the processes, benefits and limitations of using legal, formal and informal social control tactics to inform policy makers, service providers and the public. This paper characterizes barriers to a robust interdisciplinary analysis of social control and coercion in addiction treatment and provides directions for future research. APPROACH: Conceptual analysis and review of key studies and trends in the area are used to describe eight implicit assumptions underlying policy, practice and scholarship on this topic. FINDINGS: Many policies, programmes and researchers are guided by a simplistic behaviourist and health-service perspective on social controls that (a) overemphasizes the use of criminal justice systems to compel individuals into treatment and (b) fails to take into account provider, patient and public views. CONCLUSIONS: Policies and programmes that expand addiction treatment options deserve support. However, drawing a firm distinction between social controls (objective use of social pressure) and coercion (client perceptions and decision-making processes) supports a parallel position that rejects treatment policies, programmes, and associated practices that create client perceptions of coercion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".