Freins et motivations des sages-femmes libérales à l'accompagnement global de la naissance à domicile, en maison de naissance et en plateau technique
Bibliographic record
Abstract
Objective: find the motivations and barriers of independent midwives regarding global birthsupport at home, in birth centers, and in technical units, and compare them among the different locations dedicated to Global birth support (GBS).Method: observational epidemiological study conducted nationally through an anonymous on line questionnaire distributed to independent midwives from August 28, 2023, to November 30, 2023.Results: 864 questionnaires were analyzed. The main motivations for midwives for GBS include comprehensive and continuous patient support, continuous and exclusive presence of a midwife and patient/couple requests. Motivations are consistent regardless of the location. Disparities inmidwives' barriers include family reorganization, lack of liability insurance/premiums, especially in homebirths. Perceived risks are higher in home births. Midwives support improvements in liability insurance, specific billing codes, and more birthing centers. These results appear consistent with existing literature.Conclusion: common motivations for midwives for GBS, regardless of location, underscore the importance of the continuous presence of a midwife and comprehensive patient support. However, specific barriers, especially in HB, require tailored solutions. The study suggests opportunities for improvement to enhance GBS while ensuring the safety and satisfaction of professionals andpatients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".