ArticlePloS one2020
Identifying trends in nursing start-ups using text mining of YouTube content.
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.
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.
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.
Who cites it
4 citing papers in PubMed.
- Entrepreneurial Instincts of Nurses and Midwives in Ghana.Nursing open · 2025Article
- The Public's Perception of Florence Nightingale's Legacy in the Digital Media: A Critical Discourse Analysis.Nursing reports (Pavia, Italy) · 2024Article
- University innovation and start-ups: Barriers and facilitators experienced by nursing faculties - A content analysis study.Nursing open · 2024Article
- Effects of 3S business intelligence systems for nursing students: a repeated-measures randomized control trial.BMC nursing · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.