Association of electronic cigarette usage and nicotine consumption frequency of young adults in British Columbia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".