Inadequate use of prenatal services among Brazilian women: the role of maternal characteristics.
Bibliographic record
Abstract
CONTEXT: To improve the uptake of prenatal care, it is important to know how the use of prenatal care varies by maternal attitudes and social and demographic factors. METHODS: Information about social and demographic variables, prenatal care, parity, pregnancy planning, abortion attempts, satisfaction with pregnancy and satisfaction with the relationship with the child's father was collected from 611 postpartum women in Porto Alegre in southern Brazil. Multinomial logistic regression was used to evaluate associations between these variables and whether the women's use of prenatal care was adequate, partially inadequate or inadequate. RESULTS: About 40% of women had inadequate or partially inadequate prenatal care. After adjustment for other covariates, including satisfaction with the pregnancy, women having an unplanned pregnancy were significantly more likely to have had inadequate care than women who had planned their pregnancy (odds ratio, 2.0). Not living with the child's father (2.8) and dissatisfaction with pregnancy (2.1) were also associated with inadequate use of prenatal care. Women having their second or higher order birth were significantly more likely to report inadequate use of prenatal care than women having their first birth (3.9-9.0). Household income was inversely associated with inadequate use of care. CONCLUSIONS: The study suggests that maternal attitudes may be important for adequate prenatal care. Interventions should be created to encourage women with negative maternal attitudes to use prenatal care and to ensure that they have access to the care they need.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".