The Relationship Between Intimate Partner Violence and Unintended Pregnancy: Analysis of a National Sample From Colombia
Bibliographic record
Abstract
CONTEXT: Intimate partner violence is associated with a number of reproductive and mental health problems. However, the relationship between intimate partner violence and women's ability to control their fertility has not been adequately explored, especially in developing countries. METHODS: Data from the 2000 Demographic and Health Survey for Colombia were used in multivariate logistic regressions to explore the relationship between intimate partner violence and unintended pregnancy, which was included as a measure of fertility control. Regional differences in the relationship were also explored, and population-attributable risk estimates were calculated. The sample consisted of 3,431 ever-married women aged 15-49 who had given birth in the last five years or were currently pregnant. RESULTS: Fifty-five percent of respondents had had at least one unintended pregnancy, and 38% had been physically or sexually abused by their current or most recent partner. Women's adjusted odds of having had an unintended pregnancy were significantly elevated if they had been physically or sexually abused (odds ratio, 1.4); the association was observed in the Atlantica and Central regions (1.7 each), but was not significant elsewhere in the country. Eliminating intimate partner violence in Colombia would result in an estimated 32,523-44,986 fewer unintended pregnancies each year. CONCLUSIONS: These findings indicate the need to include intimate partner violence screening and treatment in reproductive health programs, to promote men's involvement in fertility control programs, and to improve the social and political response to intimate partner violence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".