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Addiction Research Centres and the Nurturing of Creativity. Substance abuse research in a modern health care centre: the case of the Centre for Addiction and Mental Health

2010· article· en· W1566523353 on OpenAlexafffund
Jürgen Rehm, Norman Giesbrecht, Louis Gliksman, Kathryn Graham, Anh D. Lê, Robert E. Mann, Robin Room, Brian Rush, Rachel F. Tyndale, Samantha Wells

Bibliographic record

VenueAddiction · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsPublic Health OntarioUniversity of TorontoWestern UniversityCentre for Addiction and Mental Health
FundersOntario Division, Canadian Mental Health AssociationOntario Ministry of Health and Long-Term Care
KeywordsAddictionMental healthSubstance abusePsychiatryHarmPsychologyHarm reductionCreativityPublic healthMedicineNursingSocial psychology

Abstract

fetched live from OpenAlex

The Centre for Addiction and Mental Health is one of the premier centres for research related to substance use and addiction. This research began more than 50 years ago with the Addiction Research Foundation (ARF), an organization that contributed significantly to knowledge about the aetiology, treatment and prevention of substance use, addiction and related harm. After the merger of the ARF with three other institutions in 1998, research on substance use continued, with an additional focus on comorbid substance use and other mental health disorders. In the present paper, we describe the structure of funding and organization and selected current foci of research. We argue for the continuation of this successful model of integrating basic, epidemiological, clinical, health service and prevention research under the roof of a health centre.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.374
Teacher spread0.329 · 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 designQualitative
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

Citations5
Published2010
Admission routes2
Has abstractyes

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