Bibliographic record
Abstract
■ AIM Identify whether centralised assessment and intake programs are useful for alcohol and drug systems. ■ DESIGN Review of evaluation findings on centralised programs in Ontario, Canada, the USA and Victoria, Australia. ■ FINDINGS Models implemented in Canada and the US operated at local level and variations were a product of settings and stakeholders. Some advances were made. The assessment and referral centres (A/Rs) in Canada accounted for around one fifth of all case loads. A greater proportion of treatment naïve people attended A/Rs and these agencies had a considerable network of services. However, A/Rs were not the hub of the system; many agencies developed their own intake structures and A/Rs took on treatment functions. In the US, only some locations with centralised intake units (CIUs) reported a greater uptake of referrals and high needs clients were more likely to attend. Centralisation resulted in improved assessments and increased levels of client satisfaction but not treatment matching. Some treatment agencies in Victoria developed centralised models for screening / assessment while referral destinations were generally in-house. In rural settings, there was cross-agency centralisation. ■ CONCLUSIONS Centralised intake requires extensive implementation planning to counter pressures that impede the potential for systems change.
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 | 0.063 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".