Analisis Faktor Sosiodemografi Dalam Pengambilan Keputusan Pemilihan Tempat Persalinan Di Kabupaten Bangkalan
Bibliographic record
Abstract
Abstract : Parents Factors, Decisions, Selection Of Delivery Places. Maternal and Infant Mortality Rate in Indonesia remains high. Approximately 95% of maternal deaths occur during labor due to obstetric complications. Efforts are made by doing delivery in health facilities so it does not happen late referred and handled and can be anticipated if maternity in health facilities. Factors that are considered to influence the decision of maternity selection by maternity mothers are socio-demographic factors, namely education & culture. High knowledge about health services causes individuals to tend to use health care facilities. This study aims to analyze the Sociodemografi Factors that Affect Decision Selection Place Birth to Maternity Mother. The research design using explanatory survey method with cross sectional design. This population are maternity mother in August-2016 with 51 samples of with multi stage sampling technique at coastal cluster, town and mountains, is Sepuluh health centers, Arosbaya health center and Galis health center. The data were taken by using quesioner and analized by Chi-Khuadrat. The results showed that the sociodemographic factor did not significantly influence the decision of maternity selection in maternal mother (p value>0,05). It is recommended that midwives further improve counseling in pregnant women in the third trimester related to preparing for the delivery process, among othersthrough.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".