Assessing patient autonomy in the context of <scp>TeamBirth</scp>, a quality improvement intervention to improve shared decision‐making during labor and birth
Bibliographic record
Abstract
BACKGROUND: Respectful maternity care includes shared decision-making (SDM). However, research on SDM is lacking from the intrapartum period and instruments to measure it have only recently been developed. TeamBirth is a quality improvement initiative that uses team huddles to improve SDM during labor and birth. Team huddles are structured meetings including the patient and full care team when the patient's preferences, care plans, and expectations for when the next huddle will occur are reviewed. METHODS: We used patient survey data (n = 1253) from a prospective observational study at four U.S. hospitals to examine the relationship between TeamBirth huddles and SDM. We measured SDM using the Mother's Autonomy in Decision-Making (MADM) scale. Linear regression models were used to assess the association between any exposure to huddles and the MADM score and between the number of huddles and the MADM score. RESULTS: In our multivariable model, experiencing a huddle was significantly associated with a 3.13-point higher MADM score. When compared with receiving one huddle, experiencing 6+ huddles yielded a 3.64-point higher MADM score. DISCUSSION: Patients reporting at least one TeamBirth huddle experienced significantly higher SDM, as measured by the MADM scale. Our findings align with prior research that found actively involving the patient in their care by creating structured opportunities to discuss preferences and choices enables SDM. We also demonstrated that MADM is sensitive to hospital-based quality improvement, suggesting that future labor and birth interventions might adopt MADM as a patient-reported experience measure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".