Unwanted or mistimed pregnancy and developmental issues in Ecuadorian children aged 3 to 5: a doubly robust estimate using data from the National Health and Nutrition Survey 2018
Bibliographic record
Abstract
Aim: To estimate the effect of unintended (unwanted or mistimed) pregnancy on early childhood development in Ecuadorian children aged 3 to 5, participating in the National Health and Nutrition Survey 2018.Methods: We used a design-based doubly robust estimate. First, we used propensity score matching to identify a subsample of children aged 3 to 5 equally likely to come from a desired vs. unintended pregnancy. Then, we used a regression model to explore the relation of maternal pregnancy intentions with early childhood development. Results: Among 1,694 observations representing 162,285 Ecuadorian children, unintended pregnancy associated with developmental delays (odds ratio: 1.56; 95% confidence interval: 1.06; 2.29), after adjusting for the household’s geographic area and income, the father’s perception of the pregnancy, the mother’s marital status, age, ethnicity, educational level, and depressive symptoms, and the child’s age, gender, and daycare/school attendance. Unintended pregnancy was also negatively associated with all four early childhood development index domains, socio-emotional development being the most affected. Discussion: Our doubly robust design found evidence of the relation between the maternal perception of pregnancy and early child development. Addressing this relation to achieve reproductive justice entails considering a wide spectrum of population health and legal interventions to allow adequate access to education, contraception, and safe abortion. Moreover, pre- and postnatal check-ups could screen for unintended pregnancy and provide support accordingly.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".