Measuring access to contraceptive methods by ethnic groups in Colombia using small areas estimation
Bibliographic record
Abstract
Afro-descendant and Indigenous women face limited access to contraceptives due to contextual barriers, partner disapproval, and educational gaps. One major challenge in designing policies to facilitate access to safe, effective, acceptable, and affordable contraceptive methods for these groups is the lack of data for informed decision-making by national and local governments. To address this information gap, we propose using small area estimation (SAE) focusing on population and ethnic groups rather than geographic areas. We estimated four state-level indicators for women by ethnic group: unmet need for family planning, family planning need satisfied, contraceptive prevalence rate, and modern contraceptive prevalence rate. SAE yielded consistent estimates with mean square errors mostly below 1% of the estimated values at both state and ethnic group levels. Contraceptive prevalence rates among Indigenous, afro-descendant, and non-ethnic women (who do not self-identify with an ethnic group) were 70.6%, 76.6%, and 81.7%, respectively. Lower contraceptive use is not determined by ethnicity. Being in a relationship increases the likelihood of contraceptive use; however, when afro-descendant and Indigenous women have partners from the same ethnic background this probability decreases. Consequently, addressing disparities in contraceptive use among ethnic groups requires government initiatives involving both women's partners and communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".