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Record W3107371335 · doi:10.1093/her/cyaa040

Efficacy of Canadian health warning statements on caffeinated energy drinks: an experimental study among young Canadians

2020· article· en· W3107371335 on OpenAlexafffundabout
Danielle Wiggers, Jessica L. Reid, David Hammond

Bibliographic record

VenueHealth Education Research · 2020
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsRecallPsychologySalience (neuroscience)CaffeineMedicineSocial psychologyEnvironmental healthCognitive psychologyPsychiatry

Abstract

fetched live from OpenAlex

The current study examined the efficacy of health warnings on caffeinated energy drinks (CEDs). Participants aged 12-24 years (n = 2040) completed an online survey where they were asked to recall any existing warning statements on CED products and were randomized to one of 29 experimental warning conditions. Regression models were fitted to examine differences between conditions in product appeal, perceived safety and message recall. Overall, fewer than 30% of participants were able to accurately describe an existing CED product warning. Experimental findings indicated that exposure to CEDs with warning labels resulted in greater recall. Warnings on the back of CED cans featuring large font, a border, and a 'caution' heading resulted in significantly greater recall (P < 0.05 for all). Front-of-package 'High source of caffeine' labels resulted in greater recall than a quantitative description (P < 0.001); caffeine labels generally elicited lower product appeal (P < 0.001) and perceived safety (P = 0.002) ratings vs. no caffeine labels, and the qualitative caffeine statement elicited lower perceived safety ratings than the quantitative statement (P = 0.02). Existing warning statements in Canada have low levels of awareness. Warnings on CEDs could be enhanced to increase the salience of messages, with greater impact from clear, descriptive, front-of-package 'High source of caffeine' labels.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.188
GPT teacher head0.528
Teacher spread0.340 · 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 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

Citations6
Published2020
Admission routes3
Has abstractyes

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