Mother Behavior Prefer Untrained Traditional Birth Attendant As Labor Support Person At Tembilahan Hulu Public Health Center Districts On 2016
Bibliographic record
Abstract
Background: Maternal Mortality Rate (MMR) is one indicator of health development in Indonesia. Deliveries by health personnel to be very important in efforts to reduce maternal mortality. Coverage of births attended by skilled health personnel at health centers Tembilahan Hulu has yet to reach the target of 90%. Target Health Center Tembilahan Hulu is 80% but reached only 45%. The number of maternal deaths in health centers Hulu 2016 Tembilahan 1 case and the number of infant mortality as much as 5 case, one cause of death is handled by TBAs. Objective: to know the mother's behavior in selecting birth attendants in health centers working area Tembilahan Hulu. Design: Qualitative research, to obtain in-depth information about how the Mother Behavior in Choosing Auxiliary Power Delivery at Puskesmas Tembilahan Hulu 2016. Methods: This study used a qualitative descriptive method, which is an approach to research that revealed certain social situations to describe reality correctly, formed by words based on the techniques of collecting and analyzing relevant data obtained from the natural situation. Results and Discussion: Research shows that mothers choose birth attendants decision is closely linked to the knowledge, attitudes, social culture, access to health services, family support. Conclusions: The behavior of mothers in selecting birth attendant is still a lot to TBAs compared to the health worker / midwife.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".