Electronic cigarettes
Bibliographic record
Abstract
Background and Purpose: Electronic cigarettes are gaining vast popularity because the perceived impression about electronic cigarettes is they are a safer alternative to conventional smoking (Belluz, 2015). As a result, more teenagers are switching to electronic cigarettes either as a smoking cessation tool, or for recreational use. However, it is supported by the evidence review that there is nicotine mislabeling between what the manufacturer has labeled and the actual nicotine content in the liquids (Goniewicz et al., 2012). This is a critical health concern for teenagers and recreational users because they are exposed to nicotine, which is a neurotoxin that creates the addiction for smoking. As a result, over a period of time, recreational electronic cigarette users have a higher chance of switching to conventional smoking (Bach, 2015). Hence, the purpose of this research is to determine whether nicotine can be found in nicotine free electronic cigarette liquids Methods: The nicotine content in the electronic cigarette liquids will be determined using Gas Chromatography - Mass Spectrometry. Inferential statistics such as a one tailed t-test will be done using Microsoft Excel and SAS to see if nicotine can be detected in nicotine-free electronic cigarette liquids and if there is a statistically significant difference. Results: The two p-values from the parametric test were 0.2811 and 0.2953. The p-value to reject the null hypothesis was set at 0.05. Because the p-values from the inferential statistics were greater than 0.05, the null hypothesis was not rejected and the actual nicotine content is equal to what the manufacturer had labeled as nicotine free. Discussion: Although the inferential statistics indicated that there was no statistical significance in nicotine concentration, two out of the ten nicotine free electronic cigarette liquids measured nicotine levels above 0 ppm. Conclusion: There was not a significant difference in nicotine concentration found in the electronic cigarette liquids and the actual nicotine concentration is equal to the labeled concentration. However, because the sample size of only ten is too small, there is a potential for type 2 error. Also, the samples came from only two manufacturers. Therefore, the results from this research are not representative for all the electronic cigarette liquids. More research should be conducted to provide scientific evidence to stop recreational electronic cigarette users from the exposure of electronic cigarettes as these could act as a stepping-stone towards smoking conventional cigarettes. Teenagers who start smoking at an early age will be more
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.630 | 0.454 |
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".