The influence of reproductive information quality on the probability of unplanned and unwanted pregnancies in Brazil
Bibliographic record
Abstract
Background: Unplanned pregnancies are a significant risk factor for inadequate use of prenatal care, and unplanned newborns are prone to having low birth weight. Women with unplanned pregnancies have a higher probability of reporting medical problems before and during pregnancy. In fact, the wellbeing of the entire household may be affected. Moreover, unplanned pregnancies have been associated with a higher social burden on taxpayers. Methods: The paper uses propensity score matching approaches to estimate the effect of having correct fertility information on the probability of having unplanned pregnancies. The data was collected from a nationally representative sample of Brazilian women between the ages of 15 and 49 years. Results: Only 26% of pregnant women have the correct information about fertility levels over the menstrual cycle. Women endowed with correct information are 12% less likely to have unwanted pregnancies and 24% less likely to have unplanned pregnancies. Conclusions: Basic fertility knowledge is an important predictor of unplanned pregnancies in Brazil, but only a small share of Brazilian women have this knowledge. More optimistically, offering access to basic fertility information to women of childbearing age can significantly decrease the instances of unplanned pregnancies, thus generating significant benefits to public health and social security systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".