Desire to Have Children Reviewed from Reproductive Health as the Impact of Natural Disasters in Palu, Indonesia
Bibliographic record
Abstract
The impact of the disaster has reduced reproductive health services to women's health and welfare.Demographically, disasters with high mortality rates can change women's birth preferences thereby contributing to an increase in births.An earthquake measuring 7.4 on the Richter scale shook central Indonesia, in Palu City to be precise, accompanied by a tsunami and liquefaction with 2,227 people killed and 965 people missing including children, and 2,537 injured.This research tries to see the extent of the role of reproductive health in the high desire to have children after the disaster, where previous research found that some areas affected by the disaster experienced an increase in fertility rates.This study aims to determine the effect of reproductive health (age, history of pregnancy, history of contraception, and parity) on the desire to have children, including their chances, and to map mothers who wish to have children after a disaster.This type of research is a survey research with a cross-sectional design.Participants in this study 382 respondents.Data analysis used the chi-square test and logistic and spatial regression analysis with an overlay approach to map the distribution of respondents who wanted to have children.The results showed that age, history of pregnancy, and contraception affected the desire to have children, with P values = 0.004, 0.043, and 0.037 which were less than 0.05.The odds ratio results show that the mother's age, history of pregnancy, and history of contraception have a probability of 0.532, 0.421, and 0.630 times the desire to have children after the disaster in Palu City.The results of the sample distribution of the desire to have children through mapping the location of the coordinates showed that most of them had the desire to have children, namely 234 respondents.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".