Evidence map›Paper›PMID 33510566›Full record

ArticleAdvances in medical sociology2019

WHEN LESS IS MORE: SHIFTING RISK MANAGEMENT IN AMERICAN CHILDBIRTH.

Kellie Owens

Open access · greenAbstract read
In one paragraph

Article in Advances in medical sociology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 67% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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.

Kellie OwensReproduction, Health, and Medicine.
Institute for Human Reproduction · US

Funding

The Penn Postdoctoral Training Program in the Ethical, Legal and Social Implications of Genetics and GenomicsT32HG009496 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI Steven Joffe · 2017 to 2026
$3.4M
NHGRI NIH HHS T32 HG009496
6 · The paper itself

Abstract

As maternal mortality increases in the United States, birth providers and policymakers are seeking new solutions to address what scholars have called the "C-section epidemic." Hospital cesarean rates vary tremendously, from 7 to 70 percent of all births. Based on in-depth, semi-structured interviews with 47 obstetricians and family physicians in the United States, I explore one reason for this variation: differences in how physicians perceive and manage risk in American obstetrics. While the dominant model of risk management encourages high levels of intervention and monitoring, I argue that a significant portion of physicians are concerned about high intervention rates in childbirth and are working to reduce cesarean rates and/or the use of monitoring technologies like continuous fetal heart rate monitors. Unlike prior theories of biomedicalization, which suggest that health risks are managed through increased monitoring and intervention, I find that many physicians are resisting this model of risk management by ordering fewer interventions and collecting less information about their patients. These providers acknowledge that interventions designed to mitigate risks may only provide an illusion of control, rather than an actual mastery of risks. By limiting interventions, providers may lose this illusion of control but also mitigate the iatrogenic effects of intervention and continuous monitoring. This alternative approach to risk management is growing in many medical fields and deserves more attention from medical sociologists.

Indexed as

cesarean sectionschildbirthknowledgematernal mortalityobstetricsRisk

Identifiers

PMID33510566
PMCPMC7840065
OpenAlexW2990548683

What OpenQuestion holds

Textmetadata
LicenceTDM
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.