A Study on the Construction of a Core Midwife-Led Total Maternal Care Program for High-Risk Pregnancies
Bibliographic record
Abstract
Objective: To analyze the care needs of high-risk pregnant women during pregnancy, delivery, and postpartum hospitalization. Additionally, to gather obstetrics staff’s suggestions for improving a total care program with core midwives leading. Methods: The study conducted semi-structured interviews with 20 high-risk pregnant women and 10 obstetricians at a tertiary hospital from August 2021 to October 2022. A descriptive qualitative study assessed their care needs and current care models. An evidence-based approach was used to evaluate guidelines and develop a draft care plan. Finally, the Delphi method refined the core midwife-led total care program. Results: The study formulated a draft for a core midwife-led care program, integrating literature and expert feedback. This program defined midwife roles with 7 service standards and 6 qualification standards. The care practice included 3 level 1, 19 level 2, and 58 level 3 entries. Management of common risk factors had detailed entries for conditions like gestational diabetes, advanced maternal age, abnormal early pregnancy weight, hypertensive disorders, and scarred uterus. Conclusion: The study offers a qualitative exploration of high-risk pregnant women’s care needs and suggests improvements based on healthcare professionals’ experiences. It provides a foundation for a midwife-led care program and proposes new research directions. The methodology combines the Ottawa research application model, evidence-based approaches, and theoretical analysis to support this program’s development.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".