Bibliographic record
Abstract
"Vaping" refers to the inhalation of aerosols produced in devices that heat liquid solutions. The aerosols may contain various additives, flavours, nicotine and other drugs such as cannabis. Nicotine is the most common psychoactive substance in vaping devices (or e-cigarettes) in Canada. While vaping has been viewed primarily as a cessation method or harm reduction strategy for smokers of combustible tobacco cigarettes, a new pattern is becoming evident in adolescents and youth (age 15-24) in Canada. In this age group, vaping is reported in increasing frequencies among those who have never smoked. This suggests the possible emergence of a de novo pattern of substance use and suggests the emergence of an unmet treatment need, vaping cessation. The mental health implications of vaping are largely unknown but available data suggest that vaping is associated with mental health changes similar to those seen with combustible tobacco cigarettes. Understanding the mental health impact of "vaping" will be challenging and research is needed. An important message from the smoking literature is that data from randomized cessation trials may be especially valuable because of complex issues of temporality and confounding connected to observational data.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".