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Record W2982529745 · doi:10.47339/ephj.2019.35

Association of electronic cigarette usage and nicotine consumption frequency of young adults in British Columbia

2019· article· en· W2982529745 on OpenAlexvenueaboutno aff
Adora Kwong, Environmental Health BCIT School of Health Sciences, Dale Chen

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsElectronic cigaretteNicotineConsumption (sociology)Environmental healthMedicineYoung adultStatisticDemographyAdvertisingGerontologyBusinessPsychiatry

Abstract

fetched live from OpenAlex

Background Electronic e-cigarette ever users has been increasing as of 2015, the most prevalent ever users being young adults aged 20-24 years old. The implication of e-cigarette ever user developing into long term users is a emerging public health concern. Methods Electronic cigarette usage frequency and nicotine consumption was measured through a self-administered online survey of young adults (n= 54). Survey was advertised through social media sites between January 2019 till February 2019. Descriptive and inferential statistic was conducted using NCSS 12 to examine the association between electronic cigarette usage and nicotine consumption. Results Among young adults aged 19 to 24 years old, the frequency of e-cigarette usage was 51% high usage, 31% no usage and 16% medium and low usage. For nicotine consumption, respondents were 25% daily, 40% no use, 18% infrequent, and 14.8% frequent. Conclusion There is an association between more frequent electronic cigarette usage and higher nicotine consumption among young adults in British Columbia. Frequency e-cigarette users were found to consumption nicotine at higher frequency then non users. Further research is needed to fully understand the extent of the relationship of if e-cigarette usage promotes daily nicotine consumption or daily nicotine consumption results in higher e-cigarette usage.

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 categoriesInsufficient 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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.247
Teacher spread0.236 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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