A Multi-Faceted Approach to Communicate the Risks Associated with E-Cigarette Use to Youth
Bibliographic record
Abstract
The use of electronic cigarettes among young people has been increasing in recent years. Electronic cigarettes are marketed in ways that attract young people and downplay the risks of these products. For example, electronic cigarettes are available in a variety of flavours, such as mint or chocolate, and it can be quite challenging for young people to understand the potential harms of these products when they are marketed through enticing advertisements under the veil of harmless flavours. Moreover, recent research has shown that electronic cigarette usage may be disproportionately higher for youth with lower socioeconomic status. It is well documented in the literature that electronic cigarettes can have a multitude of negative health impacts on young people. Electronic cigarette use affects all biopsychosocial domains, including but not limited to cardiovascular disease, pulmonary disease, renal disease, mental health, substance use, and interpersonal relationships. Given the increase in electronic cigarette use and lower levels of understanding of the harms these products can have on health, it is essential to develop additional strategies to ensure that young people are made aware of the risks associated with the use of electronic cigarettes. We propose a five factor model that aims to provide support to policymakers, educators, health care professionals, families, and youth. For policymakers, it is necessary to develop policies that limit the access the youth have to electronic cigarettes. In educational settings, educators can incorporate education on electronic cigarettes in the classroom. Health care professionals should have access to tools to ensure they feel comfortable discussing the risks of electronic cigarettes with their young patients. Additionally, families should have access to evidence-based information on the risks associated with electronic cigarettes and on how to communicate with their children about electronic cigarettes. Finally, we should aim to directly reach youth and communicate the potential risks of electronic cigarette use through the social media platforms that they frequent. Ultimately, this paper provides a model that can be used by various stakeholders involved in the public health system. Moving forward, implementing and conducting short, intermediate and long-term evaluation of this model can provide insight into its effectiveness in communicating the risks associated with electronic cigarette use for young people.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".