The Effectiveness of Obstetric Emergency Interventions in Enhancing Mother and Fetal Well-Being: A Systematic
Bibliographic record
Abstract
An obstetric emergency is a condition that can threaten the life of a pregnant woman and the fetus, which occurs during pregnancy, childbirth and the puerperium. This review aimed to analyze the effectiveness of emergency obstetric interventions based on previous studies. The study was a systematic review carried out in seven stages through three databases from Ebsco, Pubmed and Proquest. The keywords used were (pregnant women) AND (emergency intervention) AND (nursing OR nurse) NOT (systematic review). The inclusion criteria in this study were: full-text, peer review, English, articles for the last five years, subject: nursing, nurses, emergency medical care, nursing care, emergency services, and document type: articles. Based on the search database, 10,496 papers were obtained, 733 documents that matched the inclusion criteria and seven papers that fit the theme were obtained. The result that based on the initial stage of the literature search, four interventions were adequate, including ACLS training, multidisciplinary management, making coloured ribbons for premature pregnancy detection, and maternal near-miss detection tools. Interventions with less effective based on statistical test results were supportive care, home visits with IPV (intimate partner violence) interventions, and administration of CPR, defibrillation, ETT insertion, and administration of epinephrine. Nurses still carry out a few emergency developmental interventions. It is necessary to develop engagement interventions related to maternal emergencies to improve the welfare of mothers and babies. Further study is needed to strengthen the evidence base of innovative interventions for specific obstetric emergencies.
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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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".