“I had to fight for my VBAC”: A mixed methods exploration of women’s experiences of pregnancy and vaginal birth after cesarean in the United States
Bibliographic record
Abstract
BACKGROUND: Vaginal birth after cesarean (VBAC) is safe, cost-effective, and beneficial. Despite professional recommendations supporting VBAC and high success rates, VBAC rates in the United States (US) have remained below 15% since 2002. Very little has been written about access to VBAC in the United States from the perspectives of birthing people. We describe findings from a mixed methods study examining experiences seeking a VBAC in the United States. METHODS: Individuals with a history of cesarean and recent subsequent birth were recruited through social media groups. Using an online questionnaire, we collected sociodemographic and birth history information, qualitative accounts of participants' experiences, and scores on the Mothers on Respect Index, the Mothers Autonomy in Decision Making Scale, and the Generalized Self-Efficacy Scale. RESULTS: Participants (N = 1711) representing all 50 states completed the questionnaire; 1151 provided qualitative data. Participants who planned a VBAC reported significantly greater decision-making autonomy and respectful treatment in their maternity care compared with those who did not. The qualitative theme: "I had to fight for my VBAC" describes participants' accounts of navigating obstacles to VBAC, including finding a supportive provider and traveling long distances to locate a clinician and/or hospital willing to provide care. Participants cited support from providers, doulas, and peers as critical to their ability to acquire the requisite knowledge and power to effectively self-advocate. DISCUSSION: Findings highlight the difficulties individuals face accessing VBAC within the context of a complex health system and help to explain why rates of attempted VBAC remain low.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".