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Record W3204673213 · doi:10.1111/add.15702

The International Cannabis Toolkit (iCannToolkit): a multidisciplinary expert consensus on minimum standards for measuring cannabis use

2021· article· en· W3204673213 on OpenAlexaff
Valentina Lorenzetti, Chandni Hindocha, Kat Petrilli, Paul Griffiths, Jamie Brown, Álvaro Castillo‐Carniglia, Jonathan P. Caulkins, Amir Englund, Mahmoud A. ElSohly, Suzanne H. Gage, Teodora Groshkova, Antoni Gual, David Hammond, Will Lawn, Hugo López‐Pelayo, Jakob Manthey, Claire Mokrysz, Rosalie Liccardo Pacula, Margriet van Laar, Ryan G. Vandrey, Elle Wadsworth, Adam Winstock, Wayne Hall, Tom P. Freeman

Bibliographic record

VenueAddiction · 2021
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Waterloo
FundersInstituto de Salud Carlos IIIGeneralitat de CatalunyaCancer Research UKSociety for the Study of AddictionMinisterio de Ciencia, Innovación y Universidades
KeywordsCannabisMultidisciplinary approachPsychosocialNeurocognitivePublic healthPsychologyMedicinePsychiatryCognitionNursingPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The lack of an agreed international minimum approach to measuring cannabis use hinders the integration of multidisciplinary evidence on the psychosocial, neurocognitive, clinical and public health consequences of cannabis use. METHODS: A group of 25 international expert cannabis researchers convened to discuss a multidisciplinary framework for minimum standards to measure cannabis use globally in diverse settings. RESULTS: The expert-based consensus agreed upon a three-layered hierarchical framework. Each layer-universal measures, detailed self-report and biological measures-reflected different research priorities and minimum standards, costs and ease of implementation. Additional work is needed to develop valid and precise assessments. CONCLUSIONS: Consistent use of the proposed framework across research, public health, clinical practice and medical settings would facilitate harmonisation of international evidence on cannabis consumption, related harms and approaches to their mitigation.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.041
GPT teacher head0.332
Teacher spread0.291 · 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 designNot applicable
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

Citations100
Published2021
Admission routes1
Has abstractyes

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