Evidence map›Paper›PMID 32053627›Full record

ArticlePloS one2020

Identifying trends in nursing start-ups using text mining of YouTube content.

Ji Young Lim, Seulki Kim, Juhang Kim, Seunghwan Lee

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

4 authors.

Ji Young LimDepartment of Nursing, Inha University, Incheon, Korea.
Seulki KimDepartment of Nursing, Inha University, Incheon, Korea.ORCID 0000-0002-4211-0342
Juhang KimDepartment of Nursing, Far East University, Chungbuk, Korea.
Seunghwan LeeDepartment of Statistics, Inha University, Incheon, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study uses YouTube content to explore trends in nursing start-ups. YouTube content can be used to understand the current trends regarding interest and awareness in various fields. The study was conducted in three stages: text mining, Delphi survey, and comparison. The frequency and degree centrality of keywords were analyzed in the text mining stage. In the Delphi survey, the 100 most frequent keywords were classified using a synthesis framework for nursing start-ups. In the comparison stage, the results of text mining and the Delphi survey were matched using a 2x2 matrix. Text mining identified "area," "business," "competence," "idea," and "success" as the most commonly used keywords. The keywords that showed the highest level of classification agreement in Delphi were "motivation," "advice," "obstacle," "business," "charisma," and "result." In the comparison using a 2x2 matrix, "dream," "idea," "opportunity," "leadership," "success," "benefit," and "satisfaction" emerged. The results indicate that interest in nursing start-ups develops at an early stage. In order to encourage nursing start-ups, it is necessary to strengthen business skills such as finance and budgeting, establish active policy support for such start-ups, and develop new nursing start-up items appropriate for the Fourth Industrial Revolution.

Indexed as

Economics, NursingCommerceData AnalysisData MiningDelphi TechniqueHumansNursingSocial Media

Identifiers

PMID32053627
PMCPMC7018134

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.