ASSESSING E-CIGARETTE’S KNOWLEDGE AND PRACTICES AMONG COLLEGE STUDENTS AMID COVID-19 PANDEMIC
Bibliographic record
Abstract
Background: E-cigarettes are a relatively new form of smokeless tobacco that has gained significant popularity in the past decade (American Lung Association, 2020). These products contain several harmful chemicals and compounds that can have long-term health effects. Among college students, knowledge and attitude have a large impact on the E-cigarette practices among this population and hence the need to address it is essential, especially with the possible impact that COVID-19 has had on many. Methods: This study utilized a qualitative research method to gather data that is focused on the Knowledge, Attitude, and Practices of college students relating to E-cigarette usage. A 14-question survey was developed using the KAP model and distributed electronically to three different introductory health science courses. Following the distribution of the survey, a voice recorded PowerPoint containing evidence-based education and resources on E-cigarettes was distributed to the instructors to provide an educational resource to surveyed participants. The results of this study were analyzed through SPSS statistical analysis version 27.0 and Microsoft Excel version 2013. Results: Findings from this study suggests that the majority of participants (93.2%) were aware that E-cigarettes could be harmful to their health. However, 31% of participants considered E-cigarettes to be either somewhat harmful or a little harmful. Furthermore, 33.8% of the participants stated they strongly agreed, agreed, or were neutral to the statement that E-cigarettes are safe when compared to regular cigarettes. This study also found that third year students had the highest rates of E-cigarette use among participants totaling 55% of all users. Finally, this study found that 27.0% of participants had used E-cigarettes at least once in their lifetime. Since the start of the COVID-19 pandemic, 6.8% of E-cigarette users stated that they had seen an increase in their usage of these products. Conclusion: This study aimed to assess for the Knowledge, Attitude, and Practices of E-cigarette use among college students at a public university in Southern California during the COVID-19 pandemic. The results suggest that there is currently a gap in knowledge relating to E-cigarettes that needs to be addressed. Many study participants were unsure of the addictive properties of E-cigarettes and considered these products to be safe when compared to traditional cigarettes. Furthermore, there is a possible relationship between COVID-19 pandemic and increased or continued use of E-cigarettes that merits further study. The use of evidence-based education among this population is highly recommended to discourage consumption among college students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".