Do Women Have a Choice? Care Providers’ and Decision Makers’ Perspectives on Barriers to Access of Health Services for Birth after a Previous Cesarean
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Repeat cesarean delivery is the single largest contributor to the escalating cesarean rate worldwide. Approximately 80 percent of women with a past cesarean are candidates for vaginal birth after a cesarean (VBAC), but in Canada less than one-third plan VBAC. Emerging evidence suggests that these trends may be due in part to nonclinical factors, including care provider practice patterns and delays in access to surgical and anesthesia services. This study sought to explore maternity care providers' and decision makers' attitudes toward and experiences with providing and planning services for women with a previous cesarean. METHODS: In-depth, semi-structured interviews were conducted with family physicians, midwives, obstetricians, nurses, anesthetists, and health service decision makers recruited from three rural and two urban Canadian communities. Constructivist grounded theory informed iterative data collection and analysis. RESULTS: Analysis of interviews (n = 35) revealed that the factors influencing decisions resulted from interactions between the clinical, organizational, and policy levels of the health care system. Physicians acted as information providers of clinical risks and benefits, with limited discussion of patient preferences. Decision makers serving large hospitals revealed concerns related to liability and patient safety. These stemmed from competing access to surgical resources. CONCLUSIONS: To facilitate women's increased access to planned VBAC, it is necessary to address the barriers perceived by care providers and decision makers. Strategies to mitigate concerns include initiating decision support immediately after the primary cesarean, addressing the social risks that influence women's preferences, and managing perceptions of patient and litigation risks through shared decision making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it