Blogging to Quit Smoking: Sharing Stories from Women of Childbearing Years in Ontario
Bibliographic record
Abstract
This study examined the degree to which the pregnant or postpartum women, in the process of quitting smoking, felt that writing in a blog about their smoking cessation journeys helped them in their efforts to become or remain smoke free. Five women who blogged for Prevention of Gestational and Neonatal Exposure to Tobacco Smoke (a website designed to help pregnant and postpartum women quit smoking) were interviewed about their experiences as bloggers. Participants were asked to complete an online survey, which had closed-ended questions regarding their sociodemographic and smoking characteristics. Once they completed the survey, semistructured qualitative interviews were conducted over the phone. Findings suggest that blogging might combine several evidence-based behavioral strategies for tobacco cessation, such as journaling and getting support from others who use tobacco. Being part of a blogging community of women who have experienced or are experiencing similar challenges can be therapeutic and help women gain confidence in their ability to quit smoking. In conclusion, blogging may help pregnant and postpartum women quit smoking by increasing their social support and promoting self-reflection.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".