Evidence map›Paper›PMID 37404444›Full record

ArticleCureus2023

Medical Students' Socioeconomic Status and Academic Performance in Medical School.

Kencie Ely, Gemma Lagasca, Shaun Andersen, Deepal Patel, Edward Simanton

Abstract read
In one paragraph

Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  15. Impact of a USMLE Step 2 Prediction Model on Medical Student Motivations.Journal of medical education and curricular development
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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

5 authors.

Kencie ElyMedical Education, University of Nevada Las Vegas School of Medicine, Las Vegas, USA.
Gemma LagascaMedical Education, University of Nevada Las Vegas School of Medicine, Las Vegas, USA.
Shaun AndersenMedical Education, University of Nevada Las Vegas School of Medicine, Las Vegas, USA.
Deepal PatelMedical Education, University of Nevada Las Vegas School of Medicine, Las Vegas, USA.
Edward SimantonMedical Education, University of Nevada Las Vegas School of Medicine, Las Vegas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Students from lower socioeconomic groups tend to underestimate their chances of acceptance to medical school and their likelihood of success once admitted. Objective The objective of this study is to determine if socioeconomic disadvantage status is linked to lower medical college admission test (MCAT) scores and academic performance in medical school. Methods Using the Association of American Medical Colleges (AAMC) education/occupation (EO) indicator, we compared economically disadvantaged students to students with no financial disadvantage on the MCAT, Phase 1 National Board of Medical Examiners (NBME), United States Medical Licensing Examination (USMLE) Step 1, Phase 2 NBME, and USMLE Step 2 test scores. Results Medical students in the disadvantaged group scored significantly lower on the MCAT than students with no financial disadvantage. The disadvantaged group showed a non-significant lower trend in performance until USMLE Step 2. Conclusions Applicants from lower socioeconomic backgrounds may perform lower on their MCAT and early medical school benchmark exams, but they appear to catch up with and even surpass their peers on their USMLE Step 2 examination.

Indexed as

academic performancefinancial burdengpamcatmedical schoolmedical school admissionsmedical studentsocioeconomic statususmle

Identifiers

PMID37404444
PMCPMC10315161

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.