Tiered Frameworks for Planning Substance Use Service Delivery Systems: Origins and Key Principles
Bibliographic record
Abstract
It is well known that only a relatively small proportion of people in the community who experience substance use problems seek assistance from the specialized sector of services that have been commissioned to provide treatment and support for these problems. Going back to seminal reports from the early 1990s there has been a call for a systems approach to “broaden the base of treatment” in order to achieve wider coverage and yield positive outcomes at a population level. In some jurisdictions conceptual models referred to as “tiered models” have been advanced to support planning, system design and performance monitoring. This paper traces the evolution of such tiered models for substance use services and describes a recent model advanced in Ontario Canada for design of an integrated system of mental health, substance use and problem gambling services and supports. The paper concludes by highlighting key features and principles of the tiered approach that are critical for its actual operationalization. Some challenges operationalizing such a comprehensive system design framework are also noted.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".