Evidence map›Paper›PMID 36176338›Full record

ArticleHarvard public health review (Cambridge, Mass.)2021

Accessible Medical Education & TIC: Increasing Equitable Care for Disabled Patients.

Christina Jean Su, Peppar E P Cyr

Open access · greenAbstract read
In one paragraph

Article in Harvard public health review (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 16% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

2 authors at 1 institution in 1 country.

Christina Jean SuWashington University in St. Louis.ORCID 0000-0003-0968-9409
Peppar E P CyrWashington University in St. Louis.ORCID 0000-0003-3225-8236
Washington University in St. Louis · US

Funding

Individual Motor Outcome Prediction in Preterm Children Using Neonatal NeuroimagingF30HD105336 · NICHD · WASHINGTON UNIVERSITY · PI CYR, PEPPAR ELIZABETH PEI-PEI · 2021 to 2024
$171k
NICHD NIH HHS F30 HD105336
6 · The paper itself

Abstract

An estimated 1 in 4 U.S. adults has a disability, and this number continues to increase. Disabled individuals face significant healthcare inequities, including but not limited to inaccessibility and mistreatment. Our current healthcare system is ill-equipped to provide equitable care to this population. There is a lack of accessibility in healthcare environments, lack of accessible medical training to enable disabled people to become healthcare providers serving their own community, and lack of thorough medical education that encompasses care for disabled patients. Furthermore, the increased risk of trauma, as well as increased risk of medical trauma specifically, endured by disabled people puts them at greater risk of long-lasting adverse effects. In this commentary, we analyze three key areas: 1) the current state of healthcare for disabled patients, 2) disability in medical education & physician workforce, and 3) the relationship between trauma and disability. We argue that the road to more equitable care for disabled patients involves changes to medical education that address all three of these areas. Medical training should expose trainees to disability early and throughout their training, should be made more accessible to support disabled physicians, and finally, should be trauma-informed in a manner that explicitly includes caring for disabled patients and their other intersecting identities.

Indexed as

accessibilityDisabilitymedical educationtrauma-informed care

Identifiers

PMID36176338
PMCPMC9518008
OpenAlexW4298087058

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.