The Predictive Model of the Fertility Pattern of Young Women (15-24 Years Old) In South Sulawesi, Indonesia
Bibliographic record
Abstract
Teenagers that have given birth have a high chance of a total fertility rate and prevalence. The study aimed to analyze the contribution of demographic and socio-economic factors, access to information, sexual activity, and literacy on family planning on the fertility pattern of young women (15-24 years old). This research uses 2017 data from the Indonesian Demography and Health Survey (IDHS). Data analysis performed multiple logistic regression with a predictive model. The predictors of young female fertility (15-24 years old) were marital status (aOR: 373.9, 95%CI 112.7-1239.8), age of 19-21 years old (aOR: 7.74, 95%CI 2.19-27.32), age of 22-24 years old (aOR: 4.79, 95%CI 1.61-14.32), a low education level (aOR: 2.53, 95%CI 0.94-6.82), unemployed (aOR: 2.73, 95%CI 1.14-6.55) or working in agriculture (aOR: 1.16, 95%CI 0.19-6.87), and low (aOR: 1.79, 95%CI 0.73-4.41) or medium (aOR: 1.58, 95%CI 0.42-5.87) wealth index, based on SKDI's 2017 data. There needs to be an improvement in the education access to increase job opportunities and improve the socio-economic conditions of the community. This improvement will have positive impacts in preventing adolescent marriage and decreasing the fertility rate of young women
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".