Analisis Faktor yang Memengaruhi Kebutuhan Ber-KB dengan Pendekatan Social Cognitive Theory (Studi di Kecamatan Genteng Surabaya)
Bibliographic record
Abstract
Increased population growth was a problem faced by Indonesia. One of reason is because of the high unmet need for family planning. This research analyzed Influence factor family planning needs based on social cognitive theory. The research was quantitative study with a cross sectional design. Samples were married woman with the age of 15–49 years who don’t to have children or postpone their pregnancy either by using contraception or not as many as 70 womans and taken by simple random sampling. The independent variable were observational learning, outcome expectation, self efficacy, husband support, access to information, and access to health services. The dependent variable of this study was family planning needs. Quantitative data were analyzed using binary logistic regression test. A quarter of respondents were of unmet need for family planning. There was influence observational learning, husband support, and access information to family planning needs. This research concluded that was the determinant factor that influence to family planning need was access to information.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".