In search of respect and continuity of care: Hungarian women's experiences with midwifery‐led, community birth
Bibliographic record
Abstract
INTRODUCTION: To describe and compare intervention rates and experiences of respectful care when Hungarian women opt to give birth in the community. METHODS: We conducted a cross-sectional online survey (N = 1257) in 2014. We calculated descriptive statistics comparing obstetric procedure rates, respectful care indicators, and autonomy (MADM scale) across four models of care (public insurance; chosen doctor or chosen midwife in the public system; private midwife-led community birth). We used an intention-to-treat approach. After adjusting for social and clinical covariates, we used logistic regression to estimate the odds of obstetric procedures and disrespectful care and linear regression to estimate the level of autonomy (MADM scale). FINDINGS: In the sample, 99 (7.8%) saw a community midwife for prenatal care. Those who planned community births had the lowest rates of cesarean at 9.1% (public: 30.4%; chosen doctor: 45.2%; chosen midwife 16.5%), induced labor at 7.1% (public: 23.1%; chosen doctor: 26.0%; chosen midwife: 19.4%), and episiotomy at 4.44% (public: 62.3%; chosen doctor: 66.2%; chosen midwife: 44.9%). Community birth clients reported the lowest rates of disrespectful care at 25.5% (public: 64.3%; chosen doctor: 44.3%; chosen midwife: 38.7%) and the highest average MADM score at 31.5 (public: 21.2; chosen doctor: 25.5; chosen midwife: 28.6). In regression analysis, community midwifery clients had significantly reduced odds of cesarean (0.35, 95% CI 0.16-0.79), induced labor (0.27, 95% CI 0.11-0.67), episiotomy (0.04, 95% CI 0.01-0.12), and disrespectful care (0.36, 95% CI 0.21-0.61), while also having significantly higher average MADM scores (5.71, 95% CI 4.08-7.36). CONCLUSIONS: Hungarian women who plan to give birth in the community have low obstetric procedure rates and report greater respect, in line with international data on the effects of place of birth and model of care on experiences of perinatal care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".