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Record W2779878951 · doi:10.1177/145507251002700607

Tiered Frameworks for Planning Substance Use Service Delivery Systems: Origins and Key Principles

2010· article· en· W2779878951 on OpenAlexaffabout
Brian Rush

Bibliographic record

VenueNordic Studies on Alcohol and Drugs · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsOperationalizationProcess managementService systemKey (lock)Service (business)Service delivery frameworkRisk analysis (engineering)PopulationComputer scienceManagement scienceKnowledge managementBusinessEngineeringMedicineComputer securityMarketingEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.333
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2010
Admission routes2
Has abstractyes

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