SUBSTITUTION THERAPY FOR HEROIN ADDICTION
Bibliographic record
Abstract
Substitution treatment for heroin addiction, defined here as maintenance prescribing of opioid agonist drugs to opioid dependent subjects, has increased in the last decade. The recent history of substitution treatment in five countries--Canada, the U.K., Australia, Israel, and France--is reviewed. In all five countries, the critical issues around substitution treatment are similar. The first key issue concerns the balance between making treatment accessible and attractive, and minimizing diversion to the black market. The second issue concerns the role of primary health care in delivering MMT. In general, there has been increasing involvement of primary health care, with training and support for practitioners. However, there remains uncertainty and official ambivalence over whether treatment should be restricted to specialist clinics and practitioners, or available through primary care. Most importantly, underlying these issues is the problem of stigma being associated with both addiction, and with substitution treatment. The underlying problem that treatment is often at odds with community values places enormous strains on substitution treatment, and makes the treatment system vulnerable to shifting community support and abrupt, politically-driven changes in policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".