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Record W4408318433 · doi:10.1111/jomf.13091

Educational gap between partners and sterilization

2025· article· en· W4408318433 on OpenAlexaff
Kate H. Choi

Bibliographic record

VenueJournal of Marriage and the Family · 2025
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsSterilization (economics)PsychologyBusiness

Abstract

fetched live from OpenAlex

Abstract Objective This study compares the sterilization behavior of couples with varying joint education levels and union types. Background A couple's joint education levels affect the resources available to them and the power dynamics within the relationship; they also help determine which spouse takes primary responsibility for the couple's fertility work. However, few studies have examined how couples' sterilization behavior differs according to their joint education levels and union type. Methods Using data from the 2006–2019 National Survey of Family Growth, this study estimated multinomial logistic regression models to predict the relative risk of relying on female sterilization, male sterilization, or reversible contraception for couples with varying joint education levels and union type. Results Married and cohabiting couples with higher joint levels of education were less likely than their lesser‐educated counterparts to rely on female sterilization. Married couples with higher joint levels of education were more likely than their lesser‐educated counterparts to rely on male sterilization. However, for cohabiting couples, disparities in reliance on male sterilization differed little according to their joint levels of education. Conclusion Future studies should consider how male and female partners' education interact to affect their sterilization behavior. When they do, they should consider their relative and absolute levels of education.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.280
Threshold uncertainty score0.099

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.036
GPT teacher head0.366
Teacher spread0.330 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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