Birth after caesarean : an investigation of decision-making for mode of delivery
Bibliographic record
Abstract
Background: Clinical practice guidelines indicate that over 80% of women with a previous caesarean should be offered a planned vaginal birth after caesarean (VBAC), however only one third of eligible women choose to plan a VBAC. Shared decision-making (SDM) interventions support women to make choices based on their informed preferences. To facilitate implementation of SDM it is necessary to understand the patient (micro), health services (meso), and policy (macro) factors that influence decision-making. Objectives: My objective is to explore attitudes toward and experiences with decision-making for mode of birth after caesarean section in British Columbia (BC) to identify factors that influence implementation of SDM. Methods: In-depth, semi-structured interviews were conducted with women eligible for VBAC, care providers, and health service decision makers recruited from three rural and two urban BC communities. Integrated knowledge translation (iKT) principles guided study design, while constructivist grounded theory informed iterative data collection and analysis. Findings were interpreted using complex adaptive systems theory (CAS). Results: Analysis of interviews (n=57) and CAS interpretation revealed that the factors influencing decisions resulted from interactions between the micro, meso, and macro levels of the health care system. Women formed early preferences for mode of delivery (after the primary caesarean) through careful deliberation of the social risks and benefits of mode of delivery. 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 limited access to surgical resources, which had resulted from budget constraints. To facilitate mutual understanding among stakeholder groups, iKT activities included policy dialogues and the creation of a policy brief. Conclusion: To facilitate the effective implementation of SDM in clinical practice for mode of delivery after a previous caesarean section, it is necessary to address the needs of women, care providers, and decision makers. These include initiating decision support immediately after the primary caesarean, assisting women to address the social risks that influence their preferences, managing perceptions of risk related to patient safety and litigation among physicians, and access to surgical resources.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".