Smoking Motives, Quitting Motives, and Opinions About Smoking Cessation Support Among Expectant or New Fathers
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to identify smoking and quitting motives among expectant or new fathers who were in the precontemplation or contemplation stage of smoking cessation and to explore their perceptions of smoking cessation interventions. DESIGN: This study used a descriptive qualitative design. SETTING: The study was conducted in an outpatient antenatal clinic and postpartum unit of a large university hospital. PARTICIPANTS: A convenience sample of five expectant fathers and five new fathers who smoked was used. METHOD: Qualitative thematic analysis was used to analyze the transcripts of audio-recorded interviews. RESULTS: Despite their reluctance to quit smoking, all the participants made changes in their smoking behaviors during pregnancy or postpartum to protect their partners and infants from the odor and/or potential harm of secondhand and thirdhand smoke. Our findings reveal that pregnancy and childbirth may be a time when men experience additional and unique stress that influences continued smoking but may also give rise to unique motives for future smoking reduction and cessation among men previously resistant to quitting. Furthermore, expectant or new fathers may be more drawn to smoking cessation interventions that foster their own personal strategies to reduce or quit smoking and that respect their needs for self-reliance and control. CONCLUSION: The perinatal period may be an opportune time for a motivationally based proactive smoking cessation intervention among male smokers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".