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Record W4376107126 · doi:10.1080/00909882.2023.2206458

Communication and decision-making of cesarean sections in China: an exploration of both obstetricians’ and patients’ perspectives

2023· article· en· W4376107126 on OpenAlexafffund
Yuping Mao, Yadong Ji, Lu Shi, Solina Richter, Yuan Huang, Yingyao Chen

Bibliographic record

VenueJournal of Applied Communication Research · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsDistrustMedicineMaternity careFamily medicineChinaNursingObstetricsPsychologyPregnancy

Abstract

fetched live from OpenAlex

This paper addresses doctor-patient communication about C-sections (CSs) in Shanghai, China. Specifically, we examine the information discrepancies between obstetricians’ and patients’ perceptions of CS and the important factors affecting shared decision-making. We conducted semi-structured individual interviews with 12 postnatal women who experienced CS and with 12 obstetricians. We identified barriers to patient-doctor communication and shared decision-making: obstetricians’ lack of time in outpatient prenatal visits to explain the implications of CSs, family members’ knowledge and opinions on CSs, and information from media and social networks. The lack of communication between the expectant women and the obstetricians was driving the women’s distrust in the latter. The lack of obstetricians’ time to communicate with the pregnant woman led to low trust in the medical staff and overutilization of CS, whereas a lack of financial resources led to underutilization of CS. The obstetricians suggested providing expectant women with more education programs and midwifery support.

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)0.002
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 score0.786
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.125
GPT teacher head0.455
Teacher spread0.330 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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