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Record W2167419178 · doi:10.1080/08897077.2014.923362

A Canadian Perspective on Addiction Treatment

2014· review· en· W2167419178 on OpenAlexaffabout
Nady el‐Guebaly

Bibliographic record

VenueSubstance Abuse · 2014
Typereview
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAddiction treatmentPerspective (graphical)AddictionPsychologyPsychiatryPsychotherapistMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a synopsis of addiction treatment in Canada, along with some available comparative figures with other North American countries. Within the framework of Canada's Medicare, a largely single-payer system, addiction and psychiatric disorders are insured on par with other medical disorders. Canada's strategy recognizes the four pillars of prevention, treatment, harm reduction, and enforcement. The Canadian Alcohol and Drug Use Monitoring Survey is the yearly main source of data on alcohol and illicit drug use. The main features of the Canadian addiction treatment network are identified as a "top 10" list, outlining early identification and intervention, assessment, and referral; detoxification; ambulatory care/day treatment programs; residential care; hospitals; concurrent disorders networks and regionalization; drug specific strategies; mutual help; behavioral addictions; and training, qualification, and research.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.010
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.027
GPT teacher head0.329
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2014
Admission routes2
Has abstractyes

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