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Record W2491847131 · doi:10.21543/dee.2015.3

Contraceptive use in Hungary: Past trends and actual behavior

2015· article· en· W2491847131 on OpenAlexaboutno aff
Zsuzsanna Makay

Bibliographic record

VenueDemográfia · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsBirth controlCondomQuarter (Canadian coin)Family planningPillDemographyDeveloped countryPsychologyPopulationMedicineGeographySociologyResearch methodologyFamily medicine

Abstract

fetched live from OpenAlex

In the present study I look first at findings of earlier surveys on women’s birth control behavior since 1958 in Hungary. I then turn to the detailed analysis of the birth control practices of Hungarian women based on data from the Generations and Gender Survey (2008–2009). First I examine the influences on whether partnered women of reproductive age employ any birth control methods or not, and then I explore the methods that are chosen. What emerges from this examination is that modern contraception has spread widely in Hungary since the 1960s, but in 2009 a quarter of Hungarian women were still not using any method of birth control, or else they were merely using a traditional low-efficiency types. The profile of such abstainers is clear: they are reaching the end of their reproductive period, have a low level of education, are married, have financial difficulties, are generally childless, and do not plan to have a child in the short-term. The results of a multinomial logistic model show that there are also several demographic and social factors behind choosing a method of contraception. In 2009 the most common of these was the condom, followed by birth control pills (“the Pill”) and intra-uterine devices (IUDs, “the coil”). Finally, the study compares birth control in five European countries and its changes since the 1990s. The use of the Pilldeclined in all the countries in the 1990s, and only in Bulgaria and Hungary have condom users outnumbered pill-takers.

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)0.000
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 score0.128
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.093
GPT teacher head0.332
Teacher spread0.240 · 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
Published2015
Admission routes1
Has abstractyes

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