MANTRA’s Effect in Raising Pregnant Women's Awareness of Cigarette Smoke Exposure’s Impacts: A Qualitative Study
Bibliographic record
Abstract
Anemia in pregnant women is a significant health problem because it has a direct impact on the mother and fetus, including increasing the risk of low birth weight, premature birth, and maternal and infant mortality. One risk factor that is often overlooked is exposure to cigarette smoke, especially from the household environment. Pregnant women in Klaseman Village, Sukoharjo, still face exposure to cigarette smoke from their home-smoking partners. Therefore, educational interventions are needed that can increase awareness among pregnant women to avoid exposure to cigarette smoke and prevent anemia. This study used a qualitative method to evaluate the effectiveness of the MANTRA educational media (MAri hiNdari Terpapar asap Rokok, cegah Anemia), namely leaflets containing information about the definition of anemia, symptoms, impacts, prevention methods, and the relationship between cigarette smoke exposure and the incidence of anemia. Empowerment activities were carried out on five pregnant women who attended the integrated health post through lectures, distribution of MANTRA, and interviews two weeks after education. The results showed an increase in knowledge of pregnant women regarding the dangers of cigarette smoke and changes in attitudes, such as moving away from sources of smoke, neutralizing the air in the house, and educating husbands not to smoke in the house. Evaluation using the Ottawa Charter approach showed that MANTRA successfully covered five elements of health promotion: supportive public policy, supportive environment, individual skill development, health service reorientation, and community action.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".