Body image emotions, perceptions, and cognitions distinguish physically active and inactive smokers
Bibliographic record
Abstract
OBJECTIVES: To determine if body image emotions (body-related shame and guilt, weight-related stress), perceptions (self-perceived overweight), or cognitions (trying to change weight) differ between adolescents characterized by smoking and physical activity (PA) behavior. METHODS: Data for this cross-sectional analysis were collected in 2010-11 and were available for 1017 participants (mean (SD) age = 16.8 (0.5) years). Participants were categorized according to smoking and PA status into four groups: inactive smokers, inactive non-smokers, active smokers and active non-smokers. Associations between body image emotions, perceptions and cognitions, and group membership were estimated in multinomial logistic regression. RESULTS: Participants who reported body-related shame were less likely (OR (95% CI) = 0.52 (0.29-0.94)) to be in the active smoker group than the inactive smoker group; those who reported body-related guilt and those trying to gain weight were more likely (2.14 (1.32-3.48) and 2.49 (1.22-5.08), respectively) to be in the active smoker group than the inactive smoker group; those who were stressed about weight and those perceiving themselves as overweight were less likely to be in the active non-smoker group than the inactive smoker group (0.79 (0.64-0.97) and 0.41 (0.19-0.89), respectively). CONCLUSION: Body image emotions and cognitions differentiated the active smoker group from the other three groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".