E-Cigarette Dependence in Youth
Bibliographic record
Abstract
INTRODUCTION: The majority of e-cigarette vaping youth use nicotine when vaping. Some then become dependent on the nicotine, which can result in subsequent health effects. There has been limited evaluation of convergent validity of e-cigarette dependence measures for use specifically in youth. The aim of this study was to investigate and validate various e-cigarette dependence measures for use in youth populations. AIMS AND METHODS: One thousand two hundred and five Canadian youth aged 16-24 who completed a cross-sectional online survey reported vaping at least monthly and were thus included in the analysis. E-cigarette dependence was assessed using a modified Penn State Electronic Cigarette Dependence Index (PS-ECDI), the E-Cigarette Dependence Scale (EDS), a self-perceived vaping dependence question, and time to first vape after waking. Internal consistency, convergent validity, and concurrent validity of the measures were assessed. RESULTS: Both the PS-ECDI and the EDS exhibited a good degree of internal consistency (α = 0.8472 and 0.8405, respectively). All measures exhibited convergent validity against each other and against time to first vape upon waking (p < .001), as well as concurrent validity against vaping frequency and nicotine concentration (p < .001). The PS-ECDI was inferior to the EDS, self-perceived measure, and time from waking when predicting daily vaping frequency, but, along with the self-perceived measure, was superior to the EDS and time from waking when predicting monthly vaping. CONCLUSIONS: All measures exhibit convergent and concurrent validity, as well as internal consistency. Depending on the needs of the study, it would be appropriate to use any of these measures when assessing e-cigarette dependence in adolescent and young-adult populations. IMPLICATIONS: The PS-ECDI and the self-perceived measure are equally effective in predicting monthly vaping, but the self-perceived measure was superior in predicting daily vaping. Thus, the one-item self-perceived measure of dependence is appropriate for use and preferable to the 11-item PS-ECDI or the 4-item EDS in situations of limited time or where subjects are at risk of respondent fatigue, and is superior to time to first vape after waking to predict vaping frequency.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".