Exploring the strategies that midwives in British Columbia use to promote normal birth
Bibliographic record
Abstract
BACKGROUND: Rates of normal birth have been declining steadily over the past 20 years, despite the evidence of the benefits to mother and baby. This is most obvious in steadily increasing caesarean section rates across countries and studies of the factors involved suggest it may be more to do with the organization of maternity care and the preferences of healthcare providers than changes in maternal or demographic conditions. The proportion of women in British Columbia (BC) receiving care from a midwife continues to grow and there is a particular focus on promoting and supporting normal pregnancy and birth in the midwifery philosophy of care. In BC, women receiving care from a midwife are less likely to have a caesarean section and other birth interventions. METHODS: An interpretive approach, based on interpretive phenomenology was used to explore the experiences of midwives in BC of normal birth and the strategies that they use to keep birth normal. Fourteen experienced midwives were purposively selected from across the range of practice, geographical, and rural/urban contexts to participate in depth interviews. Data were analyzed using Thematic Network Analysis. RESULTS: Seven key themes were identified in the data: working with women from the early pregnancy, informing choice, the birth environment, careful watching and waiting, managing early labour, helping the woman to cope with labour, and tools in the tool kit. CONCLUSIONS: Midwives in BC work closely with women from early pregnancy to prepare them for a normal birth, and as "instruments of care" they adopt a range of approaches to support women to achieve this. The emphasis on continuity of care in the BC model of midwifery care plays a vital role in this.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".