Identifying clinical and educational difficulties of midwives in an Indonesian government hospital maternity ward: Towards improving childbirth care
Bibliographic record
Abstract
Objective: This pilot study aimed to describe the difficulties and educational needs of Indonesian midwives working in a government hospital and thereby propose possible solutions towards improving the quality of childbirth care.Methods: This study had a qualitative exploratory design. Focus group discussions were conducted with 22 Indonesian midwives working in a government hospital. Data were analyzed using content analysis.Results: These Indonesian midwives felt they faced difficulties in providing quality care such as “shortage of resources to provide health services”, “lack of resources for professional continuing education”, “insufficient evidence-based practice”, “difficulty in providing care due to cultural background”, and “challenges teaching students”. Therefore, these difficulties contributed to their uncertainty about the quality of the care they could provide. They desired continuing education to update their knowledge and skills and fill the gap between theory and actual practice. They wanted more in-depth information about “pregnancy”, “delivery”, “puerperium”, “neonates”, and “emergencies”. These topics reflected the wide range of care needed by the diverse group of Indonesian women who visited government hospital.Conclusions: Indonesian midwives working in a government hospital had difficulties in providing quality care for women with different needs and backgrounds due to the shortage of midwives, and lack of hospital beds and lack of essential equipment. Even though midwives wanted to learn or update their knowledge and skills to fill the gap between theory they learned in school and the demands of actual practice, the opportunity to have training was very limited.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".