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Record W2134224056 · doi:10.1080/03630242.2011.574793

Determinants of Tubal Ligation in Puebla, Mexico

2011· article· en· W2134224056 on OpenAlexaff
Alanna E. F. Rudzik, Susan Leonard, Lynnette Leidy Sievert

Bibliographic record

VenueWomen & Health · 2011
Typearticle
Languageen
FieldMedicine
TopicGynecological conditions and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTubal ligationLogistic regressionLigationDemographyMedicineParity (physics)ObstetricsFamily planningGynecologyPopulationResearch methodologySurgerySociologyInternal medicine

Abstract

fetched live from OpenAlex

Tubal ligation provides an effective and reliable method by which women can choose to limit the number of children they will bear. However, because of the irreversibility of the procedure and other potential disadvantages, it is important to understand factors associated with women's choice of this method of birth control. Between May 1999 and August 2000, data were collected from 755 women aged 40 to 60 years from a cross-section of neighborhoods of varying socio-economic make-up in Puebla, Mexico, finding a tubal ligation rate of 42.2%. Multiple logistic regression models were utilized to examine demographic, socio-economic, and reproductive history characteristics in relation to women's choice of tubal ligation. Regression analyses were repeated with participants grouped by age to determine how the timing of availability of tubal ligation related to the decision to undergo the procedure. The results of this study suggest that younger age, more education, use of some forms of birth control, and increased parity were associated with women's decisions to undergo tubal ligation. The statistically significant difference of greater tubal ligation and lower hysterectomy rates across age groups reflect increased access to tubal ligation in Mexico from the early 1970s, supporting the idea that women's choice of tubal ligation was related to access.

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 score0.012
Threshold uncertainty score0.501

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 head0.049
GPT teacher head0.326
Teacher spread0.276 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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