Pregnant women's responses to a tailored smoking cessation intervention: turning hopelessness into competence
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioral interventions consisting of brief counseling and the provision of self-help material designed for pregnancy have been documented as effective smoking cessation interventions for pregnant women. However, there is a need to understand how such interventions are perceived by the targeted group. AIM: To understand the cognitive, emotional, and behavioral responses of pregnant women to a clinic-based smoking cessation intervention. METHODS: In-depth interviews with women attending four antenatal clinics in Cape Town, South Africa, who were exposed to a smoking intervention delivered by midwives and peer counselors. Women were purposively selected to represent a variation in smoking behavior. Thirteen women were interviewed at their first antenatal visit and 10 were followed up and reinterviewed later in their pregnancies. A content analysis approach was used, which resulted in categories and themes describing women's experiences, thoughts, and feelings about the intervention. RESULTS: Five women quit, five had cut down, and three could not be traced for follow-up. All informants perceived the intervention positively. Four main themes captured the intervention's role in influencing women's smoking behavior. The process started with 'understanding their reality,' which led to 'embracing change' and 'deciding to hold nothing back,' which created a basis for 'turning hopelessness into a feeling of competence.' CONCLUSION: The intervention succeeded in shifting women from feeling pessimistic about ever quitting to feeling encouraged to try and quit. Informants rated the social support they received very highly and expressed the need for the intervention to become a routine component of clinic services.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".