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Record W3203969716 · doi:10.1016/j.abrep.2021.100383

Adolescent exposure to cannabis marketing following recreational cannabis legalization in Canada: A pilot study using ecological momentary assessment

2021· article· en· W3203969716 on OpenAlexafffundabout
Chelsea Noël, Christopher Armiento, Anna Koné, Rupert Klein, Michel Bédard, Deborah M. Scharf

Bibliographic record

VenueAddictive Behaviors Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsLakehead University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCannabisContext (archaeology)LegalizationSocial marketingEveningEnvironmental healthRecreationPromotion (chess)PsychologyMedicineGeographyEcologyPsychiatryBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this pilot study was to assess the feasibility of a 9-day, smartphone-based ecological momentary assessment (EMA) protocol for tracking the frequency of Canadian adolescents' exposures to cannabis marketing, their reactions to such exposures, and the context in which exposures occur in the real-world and in real-time. METHOD: Participants were n = 18 adolescents between the ages of 14 and 18 years of age. They used an EMA application to capture and describe cannabis marketing exposures through photographs and brief questionnaires assessing marketing channel and context. Participants also rated their reactions to each exposure in real-time. RESULTS: 2.3) exposures per participant during the 9-day study. Exposures tended to occur in the afternoon (45.0%) or evening (37.5%), and while participants were at home (70%) and alone (52.5%). Most exposures occurred through promotion by public figures (27.5%) or explicitly marked internet ads (27.5%). CONCLUSION: This is the first study to demonstrate the feasibility and utility of EMA to capture adolescent exposures to cannabis marketing as it occurs in participants' natural environments. Our research offers an early look at the predictable wave of cannabis advertising targeting youth and a promising approach for studying its impacts in a post-legalization context, as well as a strategy for assessing policies, such as advertising restrictions, intending to mitigate the harms of early cannabis use among youth.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.332
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 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

Citations17
Published2021
Admission routes3
Has abstractyes

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