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Record W2078113617 · doi:10.1300/j069v24n01_04

Policy Changes and the Methadone Maintenance Treatment System for Opioid Dependence in Ontario, 1996 to 2001

2005· article· en· W2078113617 on OpenAlexaffabout
Carol Strıke, Karen Urbanoski, Benedikt Fischer, David C. Marsh, Margaret Millson

Bibliographic record

VenueJournal of Addictive Diseases · 2005
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of British ColumbiaCentre for Addiction and Mental HealthVancouver Coastal HealthHealth Sciences CentreProvidence Health CareUniversity of Toronto
Fundersnot available
KeywordsMethadoneMethadone maintenanceOpiateMedicineCensusDescriptive statisticsOpioidEmergency medicineFamily medicinePsychiatryEnvironmental healthPopulationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Until recently, the availability of methadone treatment in Ontario, Canada was limited. In 1996, policy changes were introduced to increase the availability of treatment. The 5-year impact of these policy changes is assessed. METHODS: For these census data, descriptive statistics were used to examine changes in the patient and provider populations over time using data from the College of Physicians and Surgeons of Ontario Methadone Maintenance Registry of Patients and the Registry of Methadone Prescribing Physicians. RESULTS: Between 1996 and 2001, the total number of clients in treatment increased substantially from: 1595 to 7787. Over this time period, the number of physicians prescribing methadone increased from 60 to 161. INTERPRETATION: Policy changes resulted in substantial increases in the patient and provider populations across Ontario. However, the estimated low proportion of opiate users in treatment indicates that more efforts are needed to address the potential demand for treatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.105
Threshold uncertainty score0.761

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2005
Admission routes2
Has abstractyes

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Same venueJournal of Addictive DiseasesSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207