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Record W2032470685 · doi:10.1016/j.jmwh.2008.12.014

Decision Making in Patient‐Initiated Elective Cesarean Delivery: The Influence of Birth Stories

2009· article· en· W2032470685 on OpenAlexaffabout
Sarah Munro, Jude Kornelsen, Eileen K. Hutton

Bibliographic record

VenueJournal of Midwifery & Women s Health · 2009
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsCanadian Institutes of Health ResearchUniversity of British ColumbiaMichael Smith Health Research BC
Fundersnot available
KeywordsChildbirthQualitative researchMedicineVaginal deliveryExploratory researchNarrativePreferenceObstetricsCesarean deliveryNursingPsychologyPregnancySociologySocial science

Abstract

fetched live from OpenAlex

Patient-initiated elective cesarean delivery is emerging as an urgent issue for practitioners, hospitals, and policy makers and for pregnant women. This exploratory qualitative study looks at the birth stories and cultural knowledge that women use to inform the decision about an elective cesarean without medical indication. Data collection consisted of exploratory qualitative in-depth interviews with 17 primiparous women in British Columbia, Canada. Interviews revealed the influence of socially circulated birth stories and cultural narratives on their attitudes towards mode of delivery. Participants included in their decision making process both medical information and informal birth stories that were technologically inclined and confirmed their preference for cesarean delivery. Results indicate that women who participated in this study drew heavily from social and cultural knowledge in forming their decision to give birth by patient-initiated elective cesarean delivery. Although the numbers of women who request a cesarean delivery for social reasons is still small, the persuasive influence on parturient women of positive cesarean stories and negative vaginal stories must be considered. Care providers and childbirth educators need to become familiar with the social influences impacting women's decisions for mode of delivery so that truly informed choice discussions can be undertaken.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.361
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations89
Published2009
Admission routes2
Has abstractyes

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