Men’s Responses to Online Smoking Cessation Resources for New Fathers: The Influence of Masculinities
Bibliographic record
Abstract
BACKGROUND: Smoking cessation is the single most important step to preventing cancer. Drawing on previous research, Web-based resources were developed to complement a program to support expectant and new fathers to quit smoking. OBJECTIVE: The objectives of this research were to: (1) describe the responses of expectant and new fathers who smoke or had recently quit smoking to the website resources, and (2) explore how masculinities shape men's responses to and experiences with online smoking cessation resources. METHODS: Using semi-structured, individual face-to-face interviews, the Dads in Gear Web-based resources were reviewed and evaluated by 20 new fathers who smoked or had recently quit smoking. The data were transcribed and analyzed using NVivo 8 qualitative data analysis software. RESULTS: We describe the fathers' reactions to various components of the website, making connections between masculinities and fathering within 5 themes: (1) Fathering counts: gender-specific parenting resources; (2) Measuring up: bolstering masculine identities as fathers; (3) Money matters: triggering masculine virtues related to family finances; (4) Masculine ideals: father role models as cessation aids; and (5) Manly moves: physical activity for the male body. CONCLUSIONS: A focus on fathering was an effective draw for men to the smoking cessation resources. The findings provide direction for considering how best to do virtual cessation programs as well as other types of online cancer prevention programs for men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".