Gender Differences in Reasons to Quit Smoking Among Adolescents
Bibliographic record
Abstract
It is well established that many adolescents who smoke want to quit, but little is known about why adolescents want to quit and if reasons to quit differ across gender. The objective of this study was to determine if reasons to quit smoking differ in boys and girls. Data on the Adolescent Reasons for Quitting (ARFQ) scale were collected in mailed self-report questionnaires in 2010-2011 from 113 female and 83 male smokers aged 14-19 years participating in AdoQuest, a longitudinal cohort study of the natural course of the co-occurrence of health-compromising behaviors in children. Overall, the findings indicate that reasons to quit in boys and girls appear to be generally similar, although this finding may relate to a lack of gender-oriented items in the ARFQ scale. There is a need for continued research to develop and test reasons to quit scales for adolescents that include gender-oriented items.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".