Factors associated with late seeking of prenatal care in the district of Lugela-Zambézia, Mozambique
Bibliographic record
Abstract
Abstract Objectives: to analyze the factors associated with late prenatal care attendance by pregnant women at the Lugela health center in the first quarter of 2023. Methods: cross-sectional survey conducted with 205 pregnant women. Data were collected using a structured questionnaire, supplemented with information from the pregnant woman’s prenatal records. R software version 4.2.0 was used to perform inferential statistical analyses, calculating odds ratios (ORs) and constructing a logistic regression model. Categorical variables were analyzed descriptively and tested for associations with late prenatal care attendance. Multivariate logistic regression considered a significance level of 5%, with early prenatal care attendance as the dependent variable. Results: 69.3% (n=142) of the pregnant women interviewed considered prenatal care initiation late. Late prenatal care seeking was significantly associated with education (p=0.034, OR=0.228, 95%CI= −2.915 to −0.046), age (p=0.040, OR=0.506, 95%CI= −1.369 to 0.006), and pregnancy planning (p=0.009, OR=0.451, 95%CI= −1.485 to −0.106). Conclusion: the factors associated with late prenatal care seeking were essentially obstetric, socioeconomic, and sociodemographic, highlighting the need for strategies targeted at more vulnerable groups.
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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 | 0.002 |
| 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 | 0.001 | 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".