Exploring a diverse set of specifications related to associations between adolescent smoking, vaping, and emotional problems: a multiverse analysis
Bibliographic record
Abstract
PURPOSE: The epidemiological landscape of adolescent smoking, vaping, and emotional problems has drastically changed over the past two decades. Whether and why these problems co-occur remains unclear, though this understanding is crucial for global policy and prevention efforts. The nature of co-occurring problems may be influenced by different researcher decisions when defining, operationalizing, and modeling these relationships. This study uses multiverse analysis (also known as specification curve analysis or vibration of effects), which models all justifiable measurement and analytic specifications in a single sample, to unpack the impact of researcher decisions when modeling these relationships. METHODS: Multiverse analyses were done with 3,648 unique models using a longitudinal sample of 6,639 Australian adolescents (aged ∼14.7-15.7, 2021-2022). RESULTS: Consistent co-occurrence of smoking or vaping and emotional problems was seen across unadjusted or only demographic-adjusted cross-sectional models (100 %). However, the temporality of relationships, choice of confounders, and operationalization of emotional problems substantially impacted findings. Emotional problems appeared to lead to reports of past 6 month smoking more-so than the reverse (88 % vs. 9 % unadjusted/demographic-adjusted), depression-focused measures yielded more consistent associations with smoking or vaping than anxiety-focused, and certain confounders (i.e., conduct, ADHD, other substances) explained most of the associations between adolescent smoking or vaping and emotional problems. Decision related to missingness or binary versus continuous outcomes did not meaningfully impact findings. CONCLUSIONS: While adolescent smoking or vaping and emotional problems commonly co-occur, methodological choices regarding timing, definitions, and confounding significantly influence the perceived strength of these relationships. Hence, such nuances demand careful consideration when interpreting evidence for policy.
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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 | 0.071 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".