Coerced addiction treatment: Client perspectives and the implications of their neglect
Bibliographic record
Abstract
Recent work has criticized the evidence base for the effectiveness of addiction treatment under social controls and coercion, suggesting that the development of sound policies and treatment practices has been hampered by numerous limitations of the research conducted to date. Implicit assumptions of the effectiveness of coerced treatment are evident in the organization and evolution of treatment, legal, and social service systems, as well as in related legislative practices. This review builds upon previous work by focusing in greater detail on the potential value of incorporating client perspectives on coercion and the implications for interpreting and applying existing research findings. Reviewing the existing empirical and theoretical literature, a case is made for greater accuracy in representing coercive experiences and events in research, so as to better align the measured concepts with actual processes of treatment entry and admission. Attention is given to studies of the effectiveness of treatment under social controls or pressures, the connections to coercion and decision-making, and theoretical perspectives on motivation and behaviour change, including Self-Determination Theory in particular. This synthesis of the available research on coerced addiction treatment suggests that it remains largely unclear to what extent many of the commonly employed methods for getting people into treatment may be detrimental to the treatment process and longer-term outcomes. The impact of coercion upon individual clients, treatment systems, and population health has not been adequately dealt with by addiction researchers to date.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".