Harm perceptions across vaping product features: An on‐line cross‐sectional survey of adults who smoke and/or vape in the United Kingdom
Bibliographic record
Abstract
BACKGROUND AND AIMS: Vaping products are diverse with a wide variety of features, and popular products change rapidly. This study examined the features and types of vaping products that people who smoke and/or vape perceive contribute to the health harms of vaping. DESIGN, SETTING AND PARTICIPANTS: This was a cross-sectional survey co-designed with adults who smoked/vaped and pre-registered. An on-line survey (November 2022) was used of a convenience sample of adults in the United Kingdom who smoked and/or vaped (n = 494). MEASUREMENTS: As primary outcomes, respondents were asked to select any of 15 vaping product features they perceived might have any effect on the health harms of vaping (for each: selected, not selected). Independent variables were smoking/vaping status (smoke and vape; vape, formerly smoked; vape, never regularly smoked; smoke, do not currently vape); relative vaping harm perceptions [less harmful than smoking (accurate), equally/more harmful than smoking or do not know/refused (other)]. Binary logistic regressions were used to compare outcomes by current vaping/smoking status and relative harm perceptions, adjusting for age and sex. FINDINGS: Most people (54.7%) selected between one and three features. The most frequently selected were nicotine concentration (62.2%) and amount of e-liquid consumed (59.1%), followed by nicotine type (e.g. salt or freebase; 33.0%), source/purchase location (25.3%), flavours (24.7%), temperature to heat e-liquid (21.1%), heat produced by device (20.9%), e-liquid brand (20.9%), amount of emissions (18.6%), device type (e.g. disposable, pod, tank; 17.2%), material of tank (17.0%), power/wattage (13.0%), device brand (8.1%), device size (4.1%) and device weight (2.4%). Higher nicotine concentrations, more e-liquid and salt (versus freebase) nicotine were perceived to confer greater harms. Disposables were perceived as slightly more harmful than reusable devices. There were few differences by current vaping/smoking status and between those with accurate (versus other) harm perceptions of vaping relative to smoking (P > 0.05 for most contrasts, adjusting for age and sex). CONCLUSIONS: Certain features and types of vaping products [higher nicotine concentrations, more e-liquid consumed and salt (versus freebase) nicotine] were perceived to confer greater health harms among a sample of UK adults who smoked and/or vaped. Findings are consistent with pervasive misperceptions that nicotine is a major cause of harm, although e-liquid volume is likely to contribute to harms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".