Over a Decade of Drug Use Epidemiology: Implications for Strategy and Service Provision Summary of Key Findings
Bibliographic record
Abstract
4Key trends from analyses of historical drug related data in Cheshire and Merseyside ✹ As a result of treatment services reaching capacity, the number of new clients entering drug treatment decreased during the 1990s until 1998 when numbers returned to 1991 levels. ✹ There has been a substantial increase in the provision of counselling and support services in Merseyside. ✹ The proportion of drug treatment clients in receipt of a methadone prescription has decreased in recent years, with marked variations between D(A)AT areas. ✹ New clients entering drug treatment are twice as likely to be ‘discharged drug free ’ within the calendar year, when compared to ongoing clients. ✹ New clients are also twice as likely to be ‘lost to follow up ’ when compared to ongoing clients. ✹ Of those clients ‘discharged drug free ’ in 1998, a third had returned to treatment by 1999 and almost half had re-entered treatment by 2001. ✹ Over a quarter of clients ‘lost to follow up ’ re-enter treatment in the next year and of those reported ‘lost to follow up ’ in 1998 almost half had returned to treatment by 2001. ✹ A considerable number of drug users remain in long-term drug treatment; 14 % of clients reported to the Drugs Misuse Database (DMD) in 1990 were engaged in drug treatment in 2001.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".