Evidence map›Paper›PMID 33024569›Full record

ArticleAcute medicine & surgery

A text mining analysis of perceptions of the COVID-19 pandemic among final-year medical students.

Nobuyasu Komasawa, Fumio Terasaki, Takashi Nakano, Ryuichi Saura, Ryo Kawata

Open access · goldAbstract read
In one paragraph

Article in Acute medicine & surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.7field-weighted citation impact, top 18% 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

11 citing papers in PubMed, 18 citations in OpenAlex.

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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 at 1 institution in 1 country.

Nobuyasu KomasawaMedical Education Center Osaka Medical College Takatsuki Japan.ORCID https://orcid.org/0000-0002-0607-3310
Fumio TerasakiMedical Education Center Osaka Medical College Takatsuki Japan.
Takashi NakanoMedical Education Center Osaka Medical College Takatsuki Japan.
Ryuichi SauraMedical Students' Life Support Center Osaka Medical College Takatsuki Japan.
Ryo KawataMedical Education Center Osaka Medical College Takatsuki Japan.
Osaka Medical and Pharmaceutical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimThe coronavirus disease 2019 (COVID-19) pandemic has presented various challenges to medical schools. We performed a text mining analysis via essay task to clarify perceptions among final-year medical students toward the COVID-19 pandemic.

methodsWe posed the following essay question to 124 final-year medical students: "What should medical staff do during the COVID-19 pandemic; what should you do?" Responses were subjected to quantitative analysis using a text mining approach. Frequently occurring key words were extracted, followed by multidimensional scaling and co-occurrence network calculations.

resultsOf the 124 students, 123 (99.2%) responded to the essay question. The following seven key words were identified as high-frequency words: medical, infection, patient, human, myself, doctor, and information. Co-occurrence network calculations revealed that the word "medical" had a high degree of correlation with most key words, except for "doctor." The word "myself" was correlated with not only "medical" but also "infection," "human," and "doctor."

conclusionOur analysis of perceptions among final-year medical students toward the COVID-19 pandemic revealed that most medical students are strongly affected by the COVID-19 pandemic and are motivated to work as physicians among health care professionals.

Indexed as

COVID‐19educationmedical studentperception

Identifiers

PMID33024569
PMCPMC7531178
OpenAlexW3085673922

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.