Barriers and facilitators to birth without epidural in a tertiary obstetric referral center: Perspectives of health care professionals and patients
Bibliographic record
Abstract
BACKGROUND: Epidural rates are high in tertiary obstetric referral centers, even though many patients in tertiary settings might not want or need epidural analgesia. Epidural rates are influenced by factors including labor support and routine medical intervention. This study aimed to identify barriers and facilitators to birth without epidural in a Canadian tertiary center, from the perspectives of doctors, nurses, and patients. METHODS: In this qualitative exploratory study, individual, semi-structured interviews were conducted in 2016 with 5 doctors, 5 nurses, and 4 patients who intended to birth without epidural. Interviews were audio-recorded, transcribed, and analyzed using inductive qualitative thematic analysis. RESULTS: Several contextual factors in the tertiary center facilitated or were barriers to birth without epidural. The following themes emerged: (1) differing perceptions of pain, (2) being ready for things to go wrong, (3) labor support is more labor intensive, and (4) having insufficient resources for birth without epidural. CONCLUSIONS: Reconciling patient birth goals with staff focus on patient safety is challenging in the tertiary context. Discrepancies between health care professional and patient attitudes about childbirth pain may influence decision-making about epidural use. Maintaining labor support skills is challenging for health care professionals who have limited exposure to birth without epidural. There is a need to allocate dedicated resources to better support birth without epidural. Specifically, support could be improved through the implementation of guidelines for assessment and management of labor pain, provision of a variety of pain management options, and labor support training for health care professionals.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".