Mapping and assessing sexual and reproductive health policy changes over time in Colombia: measuring their impact on pregnancy terminations
Bibliographic record
Abstract
Abstract Sexual and reproductive health and rights policies (SRHRPs) and their association with reproductive and non-reproductive behavior require precise theoretical and methodological frames. By studying the case of Colombia, we move forward with a comprehensive framework that considers simultaneously multiple SRHRP conceptualizations and their impacts over time on induced pregnancy terminations (IPT). With a mixed-method approach, we first map the evolution of SRHRPs and then analyze their direct and indirect effects on IPTs, using the provision of contraceptive methods by the government, female use of contraceptive methods, and conversations with health professionals in a mediation approach. We build a unique data set from more than 2100 policy documents, and then use data on 81,760 women (20–40 years) from four waves (2000–2015) of Colombia’s Demographic and Health Surveys. We find that SRHRPs are directly associated with an 18% reduction in reported IPTs. Associations between these variables are explained by the increased use of modern contraceptive methods (6%), and the government’s provision of those contraceptive methods (13%). Studies interested in the impact of SRHRPs need to consider not only the direct effects of legal changes on abortion outcomes but also show changes over time may operate through different sub-programs embedded in these policies, such as access to contraceptive methods and family planning. This will add further nuances to how SRHRPs are both multilayered and implemented.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".