Indicators of prenatal care received by Family Health Strategy users in cities of the State of Paraíba
Bibliographic record
Abstract
Introduction: Prenatal care benefits maternal and neonatal health. Objective: To evaluate indicators of prenatal care of users from Family Health Strategy in cities of the State of Paraíba, Brazil, and to investigate differences according to the social context and the health team. Methods: Cross-sectional study based on the questionnaire application to 897 individuals. Multivariate logistic regression was performed to verify the association between social characteristics and type of health team with indicators of the prenatal care (time of beginning, number of consultations for gestational age at delivery, use of ferrous sulfate, vaccination against tetanus before or during the gestation), treated as dependent variables. Results: Among interviewees, 81.0% began prenatal care in the first quarter of pregnancy and 83.0% had at least six consultations. Ferrous sulfate use and tetanus immunization were reported by respectively 94.9% and 88.8% of the interviewed women. Participants living with a partner, with higher socioeconomic level, and not participating in the Bolsa Família Program were more likely to have adequate beginning time of prenatal care, number of consultations and supplementation with ferrous sulfate. Beginning of prenatal care in the first quarter and having at least six consultations were associated with low food insecurity, while maternal work outside the home, high social support, family functionality and attendance by teams from Programa Mais Médicos favored the prenatal beginning time. Conclusion: The study showed satisfactory indicators of prenatal care, influenced by the socioeconomic characteristics and the social support of the pregnant woman.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".