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Record W4416394636 · doi:10.1215/00703370-12319849

The Consequences of Community Violence for Contraceptive Use and Provision in Mexico

2025· article· en· W4416394636 on OpenAlexaff
Signe Svallfors, Mónica L. Caudillo, Orsola Torrisi

Bibliographic record

VenueDemography · 2025
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsMcGill University
Fundersnot available
KeywordsFamily planningFertilityHomicidePublic healthDomestic violencePopulationPoison controlSuicide preventionMarital status

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.330
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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