How Do Physicians and Midwives Manage the Third Stage of Labor?
Bibliographic record
Abstract
BACKGROUND: Current practice guidelines recommend active management of the third stage of labor. We compared practices of three maternity care provider disciplines in management of third-stage labor and the justifications for their approach. METHODS: This study is a cross-sectional survey of maternity practitioners in usual practice settings in British Columbia. All 199 obstetricians, all 82 midwives, and a random sample of family physicians practicing intrapartum maternity care (one-third, or 346) were surveyed The three main outcome measures by discipline were the method preferred in managing third-stage labor, the reasons given for the chosen method, and views on the appropriateness of the current third-stage labor guideline. RESULTS: The overall response rate was 57.8 percent. Response rates indicating that the participants were "aware of guideline" were the following: obstetricians, 85.3 percent; family physicians, 53.7 percent; and midwives, 97.8 percent. Response rates indicating that the participants "agreed with guideline" were the following: obstetricians, 95.2 percent; family physicians, 97.6 percent; and midwives, 51.2 percent. Response rates indicating that "oxytocin should be given with anterior shoulder" were the following: obstetricians, 71.1 percent; family physicians, 68.3 percent; and midwives, 26.7 percent. Response rates indicating that "routine active management of third stage of labor should be the norm" were the following: obstetricians, 79.2 percent; family physicians, 60.2 percent; and midwives, 17 percent. All results were statistically significant (p < 0.01). CONCLUSIONS: A major difference was found between physicians and midwives in the management of third-stage labor. Physicians routinely implemented active management of the third stage of labor; midwives preferred expectant approaches, principally based on women's preference. Provincial data did not show differences in postpartum hemorrhage or transfusion rates by practitioner type.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".