Electronic Cigarettes: The Issues behind the Moral Quandary
Bibliographic record
Abstract
Electronic cigarettes (e-cigarettes), along with other nicotine delivery systems which largely became commercially available in the late 2000s, represent the most recent products in the evolutionary trend of smoking habits and have been the subject of several arguments within the global public health community and beyond. This chapter undertakes a holistic examination of different issues raised by scientists and policy makers regarding e-cigarette design, manufacture, marketing and use, as well as their resultant impact on tobacco-related outcomes at individual and population level, including potential health hazards. Based on the predominant type of tobacco product used and the evolution of the global tobacco epidemic, three new, alternative stages of the tobacco epidemic are proposed: stage 1 - predominant use of traditional tobacco products in countries that are still in the earlier stages of tobacco product use (South Eastern Asian countries, China, Middle Eastern countries and those in the African region); stage 2 - combined use of traditional and modified-risk tobacco products in countries with developing tobacco control initiatives which are still at the apex of the mortality epidemic for males and females (Southern and Eastern European, certain Asian and South American countries), and stage 3 - rapidly increasing use of modified-risk tobacco products in countries with relatively advanced tobacco control, steady reductions in cigarette sales and consumption, and a rapid increase in the popularity and application of harm reduction strategies (USA, Canada, Australia and countries in Northwest Europe). In stage 3 countries, the sudden exposure to e-cigarettes has created a moral quandary from a public health perspective. On the one hand, e-cigarettes may represent a potential game changer for smoking cessation efforts or be part of a harm reduction strategy. On the other hand, they are an untested product that could reinforce or renormalize nicotine addiction, and hence potentially undermine the effectiveness of evidence-based tobacco control activities which have contributed to declines in smoking rates over the past decades. Thus, a major public health concern regarding e-cigarettes is whether they serve as a potential gateway to nicotine addiction and subsequent tobacco use. Finally, the chapter summarizes the regulatory measures which are requested for the use of e-cigarettes as part of comprehensive tobacco control.
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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 | 0.057 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.078 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.027 | 0.053 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".