The Consequences of Community Violence for Contraceptive Use and Provision in Mexico
Bibliographic record
Abstract
This study examines the relationship between community violence and the use and provision of contraception in Mexico, where family planning is a long-standing policy priority and the "war on drugs" has led to chronically high levels of violence. We adopt a two-step approach. First, we investigate the association between women's exposure to violence and first contraceptive use. Combining individual-level data (n = 86,219) from two waves of the National Survey of Demographic Dynamics (ENADID) with information on monthly municipality-level homicides in event-history models, we analyze the timing and method of women's first contraceptive use and the source of first contraception. Second, leveraging rare data from Mexico's Ministry of Health in clinic fixed-effects models, we study the association between homicides and contraceptive provision from public clinics. Results show strong positive associations between community violence and both the transition to first contraceptive use and the contraceptive provision of reversible methods. These relationships are stronger in the long term; one more homicide per 10,000 population during the past five years is associated with triple the risk of initiating contraceptive use and two to three more reversible contraception users served in each public clinic per month. The findings suggest increasing contraceptive vigilance and fertility regulation preferences-but also healthcare system resilience-in times of insecurity.
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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 | 0.004 |
| 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 | 0.001 |
| 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".