Managing the Second Stage of Labor: Using Evidence to Guide Practice
Bibliographic record
Abstract
ABSTRACT Objective: The primary objectives of this exploratory study were: (1) review the literature to identify evidence‐based labor management interventions, (2) re‐evaluate the “average” length of labor associated with good childbirth outcomes, and (3) determine if there is a consensus among labor and delivery nurse managers regarding the need to revise the Friedman's Labor Curve. Design: This pilot study utilized a comprehensive literature review and an anonymous cross‐sectional survey design. Surveys were mailed to 500 maternity care agencies in the United States, Canada, and Mexico with a return rate of 17.8% (n = 89). Each participating agency was asked to submit five patient cases to be included in the analysis. Sample and Setting: The sample of patient cases (n = 419) was drawn from randomly selected maternity care agencies throughout North America representing all sizes of agencies and geographic locations. The cases submitted for analysis represented women 14 to 44 years of age with varying ethnicities who received no regional anesthesia or oxytocin augmentation or induction. Results: The average length of the second stage of labor for women today is similar to the length described by Friedman in 1954. However, a wider range of “normal” was found in the current study. A review of literature suggests non‐directive pushing and a greater variety of birthing positions improves second‐stage labor outcomes. Most (87.6%) nurse managers believed that Friedman's Labor Curve should be revised to meet the needs of current patient populations, technological advances, and nursing responsibilities. Conclusions: The parameters, assessments, and interventions currently employed during the second stage of labor need to be re‐evaluated to incorporate the most recent evidence to support best practices.
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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 | 0.111 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.026 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".