Indigenous Women’s Experiences of Smoking and Quitting Smoking in Pregnancy: A Phenomenological Study
Bibliographic record
Abstract
BACKGROUND: Maternal smoking during pregnancy (MSDP) is an important public health concern because of potential adverse health effects to the woman, fetus, and child after birth. Prevalence rates are high among groups with socioeconomic disadvantage, including Indigenous women. PURPOSE: This study was conducted to understand experiences of MSDP for Indigenous women. METHODS: The study was conducted using phenomenology. Data were collected through interviews with 15 pregnant and postnatal Indigenous women who had smoked during pregnancy. The data were analyzed for themes using phenomenological methods. RESULTS: The women's narratives revealed four experiences: quitting smoking during pregnancy to protect the unborn baby from harm; quitting smoking during pregnancy because of personal adverse health effects; cutting down smoking during pregnancy and feeling remorse for not quitting; and keeping on smoking during pregnancy and not planning to try to quit. The women's experiences also indicated several impediments to quitting smoking. CONCLUSIONS: There is need for health care policy to ensure adequate smoking cessation services and support for Indigenous women who smoke in pregnancy. Health care professionals should provide individualized interventions that take into account the challenges to quitting that pregnant women experience and that are in accordance with clinical practice guidelines for MSDP.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".