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Record W4226058580 · doi:10.1093/ntr/ntac088

Exposure to Negative News Stories About Vaping, and Harm Perceptions of Vaping, Among Youth in England, Canada, and the United States Before and After the Outbreak of E-cigarette or Vaping-Associated Lung Injury (‘EVALI’)

2022· article· en· W4226058580 on OpenAlexafffundabout
Katherine East, Jessica L. Reid, Robin Burkhalter, Olivia A Wackowski, James F. Thrasher, Harry Tattan‐Birch, Christian Boudreau, Maansi Bansal‐Travers, Alex C Liber, Ann McNeill, David Hammond

Bibliographic record

VenueNicotine & Tobacco Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsActuaUniversity of Waterloo
FundersNational Cancer InstituteNational Institutes of HealthPublic Health Agency of CanadaCancer Research UKHealth CanadaSociety for the Study of AddictionPublic Health AgencyCanadian Institutes of Health ResearchNational Institute for Health and Care Research
KeywordsHarmWorryOutbreakDemographyMedicineEnvironmental healthPsychologyPsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

INTRODUCTION: Little is known about the international impact of E-cigarette or Vaping-Associated Lung Injury ('EVALI') on youth perceptions of vaping harms. METHODS: Repeat cross-sectional online surveys of youth aged 16-19 years in England, Canada, and the United States before (2017, 2018), during (2019 August/September), and after (2020 February/March, 2020 August) the 'EVALI' outbreak (N = 63380). Logistic regressions assessed trends, country differences, and associations between exposure to negative news stories about vaping and vaping harm perceptions. RESULTS: Exposure to negative news stories increased between 2017 and February-March 2020 in England (12.6% to 34.2%), Canada (16.7% to 56.9%), and the United States (18.0% to 64.6%), accelerating during (2019) and immediately after (February-March 2020) the outbreak (p < .001) before returning to 2019 levels by August 2020. Similarly, the accurate perception that vaping is less harmful than smoking declined between 2017 and February-March 2020 in England (77.3% to 62.2%), Canada (66.3% to 43.3%), and the United States (61.3% to 34.0%), again accelerating during and immediately after the outbreak (p < .001). The perception that vaping takes less than a year to harm users' health and worry that vaping will damage health also doubled over this period (p ≤ .001). Time trends were most pronounced in the United States. Exposure to negative news stories predicted the perception that vaping takes less than a year to harm health (Adjusted Odds Ratio = 1.55, 1.48-1.61) and worry that vaping will damage health (Adjusted Odds Ratio = 1.32, 1.18-1.48). CONCLUSIONS: Between 2017 and February-March 2020, youth exposure to negative news stories, and perceptions of vaping harms, increased, and increases were exacerbated during and immediately after 'EVALI'. Effects were seen in all countries but were most pronounced in the United States. IMPLICATIONS: This is the first study examining changes in exposure to news stories about vaping, and perceptions of vaping harms, among youth in England, Canada, and the United States before, during, and after 'EVALI'. Between 2017 and February-March 2020, youth exposure to negative news stories, and perceptions of vaping harms, increased, and increases were exacerbated during and immediately after 'EVALI'. By August 2020, exposure to negative news stories returned to 2019 levels, while perceptions of harm were sustained. Exposure to negative news stories also predicted two of the three harm perception measures. Overall, findings suggest that 'EVALI' may have exacerbated youth's perceptions of vaping harms internationally.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.330
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations36
Published2022
Admission routes3
Has abstractyes

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