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Record W4388680969 · doi:10.30604/jika.v8i3.2287

Women's Participation in The Selection of Contraceptive Methods – Knowledge, Attitude and Culture

2023· article· en· W4388680969 on OpenAlexaboutno aff
Meriana Barreto Amaral

Bibliographic record

VenueJurnal Aisyah Jurnal Ilmu Kesehatan · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsFamily planningPillQuarter (Canadian coin)PopulationMedicineFamily medicineDeveloping countryDemographyGynecologyResearch methodologyNursingEnvironmental healthSociologyEconomic growthGeography

Abstract

fetched live from OpenAlex

Timor Leste is one of the developing countries with one of the main problems faced is the population sector, namely the high rate of population growth. The purpose of this study is for what factors influence the knowledge, attitudes and culture of women to participate in the selection of contraceptive methods at the Formosa Health Center. This research method is carried out descriptively with a quantitative approach. The population of this study was Women of reproductive age who sought family planning services at the Formosa Health Center. The results of this study revealed that most participants or women sought family planning services at the Formosa Health Center, dominated by women from the age group of 20 to 24 years and with a higher level of education. We found that there was little information regarding the type of use and source of information, participants with knowledge and level of education preferred to use artificial methods, especially the most widely used ones were Injections, compared to Pills, IUD and Implant. It is highlighted that the side effects of the injection contraceptive method are dizziness and headache. However, there is no fundamental reason to stop use. It is emphasized that only a quarter of women have the knowledge that the advantage of using contraceptives is only to prevent pregnancy.

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 score0.129
Threshold uncertainty score0.402

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 head0.033
GPT teacher head0.408
Teacher spread0.374 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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