Characteristics of Contraceptive Acceptors to the Use of Contraceptive Types
Bibliographic record
Abstract
Background: The success of family planning programs in Indonesia is influenced by several factors, including socio-economic, cultural, educational, religious, and women's status. At the South Magelang Health Center in 2022, it can be seen that in the first Quarter of 2022 there are 31 acceptors, both new and old acceptors with types of contraceptive method users injection 21, IUD 7, and Implant 2 acceptors Methods: the type of quantitative research with survey methods and data collection time with a cross-sectional approach. The research instrument used a questionnaire. The population is 31 respondents with data analysis using Chie Square with alpha 5%. Results: Based on statistical tests, the results obtained: there is no relationship between economic level p: 0.158, maternal age with p: 0.131, number of children with p: 0.887, education level with p: 0.778, level of knowledge about contraception with p: 0.642 and family support with p: 0.776 with the use of contraceptives. Conclusion: extensive and detailed information about various contraceptives is carried out before a person chooses to use certain types of contraception and husband support is needed in determining the type of contraception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".