Empirical support for a multi‐dimensional model of sensations experienced by youth during their initial smoking episodes
Bibliographic record
Abstract
AIMS: To examine the dimensionality of sensations experienced during initial tobacco smoking. DESIGN: Cross-sectional survey. SETTING: Thirteen secondary schools located in British Columbia, Canada. PARTICIPANTS: Data from 1187 adolescents who responded 'yes' to the question: 'Have you ever tried cigarette smoking, even one or two puffs?'. MEASUREMENTS: Participants answered questions about their demographic characteristics, tobacco smoking history and sensations experienced during their initial smoking episodes. FINDINGS: The sensations appear to represent the following three separate but modestly correlated dimensions: a pleasant dimension defined by feeling good and relaxed; an unpleasant dimension defined by coughing, feeling sick and nervous; and a 'buzz' dimension defined by feeling high and dizzy. The three factors made statistically significant contributions to the prediction of transition to regular smoking (defined as having smoked at least 100 cigarettes in one's life-time) after adjusting for age, sex and age at first puff. CONCLUSIONS: The results suggest that three relatively distinct physiological systems appear to explain the relationship between initial smoking sensations and probability of becoming a regular smoker. Researchers examining sensations experienced during initial tobacco smoking episodes should consider using a three-dimensional profile of symptoms composed of pleasant, unpleasant and buzz dimensions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".