Exploration of High Risk Pregnancy Early Detection Model for Cadre in the Working Area of Rasimah Ahmad Public Health Center Bukittinggi, West Sumatera Province, Indonesia
Bibliographic record
Abstract
The decreasing number of MMR and IMR can be achieved if the number of high-risk pregnant women decreases. To anticipate this, an approach should be made through individuals who are closest to the community to provide information about high-risk pregnancies such as health cadres. However, cadres' knowledge and attitudes regarding their roles and duties as assistants for high-risk pregnant women and early detection of high risk are still very low. Therefore, it is necessary to increase the knowledge and attitude of health cadres by using appropriate and effective learning media sources in accordance with their knowledge and needs. The general purpose of this study is to explore and identify the perspectives and experiences of health cadres in providing assistance to high-risk pregnant women. The study uses the qualitative research method . It was conducted in the working area of Rasimah Ahmad Bukittinggi Health Center in July - October 2018. The subjects of this study consisted of health cadres, KIA program designers, and policy makers. The data were collected by using in Depth Interview and Focus Group Discussion. They were analyzed by using interactive analysis method.The result of the study shows that there is still a lack of knowledge of cadres regarding their roles and duties as assistants for high-risk pregnant women and early detection of high risk of pregnancy. This is due to the absence of handbook for cadres in providing information / and counseling to regnant women other than the KIA books they have been using and the experiences they have gained so far. The conclusion of this study is there is lack of learning media sources for cadres in providing services to pregnant women. Hence, the learning media resources are urgently needed as a reference in giving quality assistance.
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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 | 0.002 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".