Evidence map›Paper›PMID 37459454›Full record

ArticleMedical anthropology quarterly2023

Epistemic Silences and Experiential Knowledge in Decisions After a First Cesarean: The case of a vaginal birth after cesarean calculator.

Nicholas Rubashkin

Open access · hybridAbstract read
In one paragraph

Article in Medical anthropology quarterly, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Nicholas RubashkinDepartment of Obstetrics, Gynecology, & Reproductive Sciences, University of California at San Francisco, San Francisco, United States.ORCID 0000-0002-6955-0532
Global Brain Health Institute · US

Funding

WOMEN'S REPRODUCTIVE HEALTH RESEARCH CENTERK12HD001262 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Andrea Vashti Jackson · 1998 to 2026
$9.3M
Effect of a patient-centered decision app on TOLAC: An RCTR01HD078748 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KUPPERMANN, MIRIAM · 2014 to 2018
$3.2M
NICHD NIH HHS K12 HD001262NICHD NIH HHS R01 HD078748
6 · The paper itself

Abstract

Evidence-based obstetrics can employ statistical models to justify greater use of cesareans, sometimes excluding experiential elements from informed decision making. Over the past decade, prenatal providers adopted a vaginal birth after cesarean (VBAC) calculator designed to support patients in making informed decisions about their births by estimating their probability for a VBAC. Among other factors, the calculator used race and ethnicity to make its estimate, assigning lower probabilities for a successful VBAC to Black and Hispanic patients. I analyze how a diverse group of women and their providers engaged with the VBAC calculator. Some providers used low calculator scores to remove a shared decision-making model by prescriptively counseling Black and Hispanic women who desired a VBAC into undergoing repeat cesareans. Consequently, women racialized by the calculator as Black or Hispanic used experiential knowledge to challenge the calculator's assessment of their supposed lesser ability to give birth vaginally.

Indexed as

Vaginal Birth after CesareanAnthropology, MedicalBlack or African AmericanCounselingDecision Making, SharedFemaleHispanic or LatinoHumansPregnancyRacismalgorithmscesarean birthevidence-based medicinefeminist anthropologyracism

Identifiers

PMID37459454
PMCPMC10993819
OpenAlexW4384500147

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.