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Record W4400615383 · doi:10.22374/cjmrp.v2i1.179

MIDWIFERY CARE FOR W O M E N PLANNING VAGINAL BIRTH AFTER CAESAREAN: A SURVEY OF O N TA R I O MIDWIFERY PRACTICES

2024· article· en· W4400615383 on OpenAlexaboutno aff
Christine Sternberg, Patricia McNiven

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsObstetricsVaginal birthMedicineGynecologyPregnancy

Abstract

fetched live from OpenAlex

Due to new evidence, the safety of vaginal birth after Caesarean (VBAC) si again generating controversy ni maternity care. Asurvey of midwifery practices was undertaken ot determine midwifery practice patterns and the factors that influence their care of women with aprior Caesarean birth. Atwo-part questionnaire, pertaining to practice characteristics and VBAC care, was mailed to al 40 Ontario midwifery practices. Data from closed-ended questions were analysed using SPSS, while open-ended responses were examined for common themes. A survey response rate of 92.5% was obtained. While 73% of practices that responded reported that they wil not accept women with more than one lower segment Caesarean birth into care, 65% indicated that they wil attend home births for women with one prior Caesarean birth. For practices that do not atend women with aprior Caesarean birth at home, prohibitive hospital policies (64%) and lack of obstetrical support (86%) were the most frequently reported reasons. Concern about increased risk and lack of experience were also identified. Variations ni practice patterns do not appear ot be related to geographic practice characteristics. The survey has identified some of the factors that influence how Ontario midwives care for women with a prior Caesarean birth. Hospital policies and support from consultant obstetricians appear to have a greater impact on midwifery care for women with prior Cacsarean birth than midwives' perception of increased risk or lack of experience.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.280
GPT teacher head0.515
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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