Overview of E-Cigarette (Vape) Usage Behavior in 2021 UMJ FKM Students
Bibliographic record
Abstract
E-cigarettes were first created in a modern way by a pharmacist from China in 2003 and patented in 2004 and then began to spread throughout the world. The results of a survey conducted by the International Tobacco Control Survey in America, Canada, Australia, and England, currently 29% of former smokers use electronic cigarettes, 7.6% have tried using electronic cigarettes and 46.6% are aware of the existence of electronic cigarettes. Some conditions that can arise from long-term use of nicotine are increased blood pressure and heart rate, as well as an increased risk of developing insulin resistance, type 2 diabetes, and heart disease. This research uses a qualitative approach with a descriptive method. Based on in-depth interviews with the informants, it was found that the informants had various reasons for using vapor, both for health reasons and for environmental reasons. All resource persons also argue that the dominant factor that makes a person use vapor is environmental factors. Meanwhile, when viewed from the behavior of all informants, they do not use vapor continuously but at certain times, even one of the informants still uses conventional cigarettes to be accompanied by vapor.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".