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Record W1994277315 · doi:10.1080/13698575.2014.911823

Cannabis, risk and normalisation: evidence from a Canadian study of socially integrated, adult cannabis users

2014· article· en· W1994277315 on OpenAlexaffabout
Cameron Duff, Patricia G. Erickson

Bibliographic record

VenueHealth Risk & Society · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCannabisPublic healthPsychologyQualitative researchPerceptionPsychiatryMedicineSociologyNursing

Abstract

fetched live from OpenAlex

Howard Parker’s ‘normalisation thesis’ has made a significant contribution to youth drug studies in many countries. Parker’s thesis has been less investigated, however, for its application across the life course, and few scholars have considered its utility for assessments of the meaning and experience of drug-related health risks. This article adds to discussions of drugs, normalisation and risk by analysing qualitative data collected from 165 long-term cannabis users aged 20–49 years in four Canadian provinces between 2008 and 2010. We focus on participants’ assessments of the risks and harms associated with persistent cannabis use and the strategies they employed to mitigate these risks. Our findings indicate important distinctions between culturally mediated conceptions of cannabis-related risks and the more narrowly grounded perception of cannabis harms based on personal or peer experience. These distinctions correspond with participants’ reports of a significant shift in cannabis’ risk profile in Canada. Participants attributed this shift to three factors: the growing prevalence of cannabis use; the rise of ‘medical marijuana’ and renewed attention to the drug’s therapeutic benefits and what they perceived to be the low incidence of cannabis-related harms in Canada. We conclude that understanding how health risks are assessed and managed by cannabis users should help to clarify how and why more tolerant attitudes about cannabis have emerged in Canada and how this change may impact on non-users’ expectations about any future initiation of use. We close by reflecting on the implications of our findings for cannabis-related public health, education and harm reduction initiatives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.036
GPT teacher head0.341
Teacher spread0.306 · 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.

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

Citations52
Published2014
Admission routes2
Has abstractyes

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