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Articles

Bibliometric study of traumatic brain injury rehabilitation

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Pages 51-68 | Received 27 Jan 2019, Accepted 05 Jul 2020, Published online: 02 Aug 2020
 

ABSTRACT

Purpose: This paper aims to present a bibliometric analysis of scientific documents in the field of traumatic brain injury rehabilitation.

Methods: Web of Science was used to collect bibliographic data of traumatic brain injury rehabilitation documents from 1983 until the end of 2017.

Results: Of a total of 6069 documents retrieved, 78.2% were journal articles. The average annual growth of the documents as of the year 2000 was 9.4%. The most frequent subject categories in this field were Rehabilitation, Neurosciences and Neurology, Sport Sciences, Psychology, and General and Internal Medicine. The most active journal was Brain Injury. More than 50% of the documents were published in 10 journals. The most prolific and impactful institutions were from the USA, Australia and Canada. Traumatic brain injury, rehabilitation, brain injury, stroke and outcome were the most commonly used keywords. Mild traumatic brain injury and concussion were the topics receiving attention in recent years.

Conclusion: Traumatic brain injury rehabilitation is a young and constantly growing field. Since the late 1990s, traumatic brain injury rehabilitation documents published yearly comprised about 3–4% of all rehabilitation documents. It was shown that review papers and proceedings have more impact than journal articles, and collaborative papers receive more citations. It was also revealed that knowledge does not become obsolete rapidly in this field.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

3 “A scientist has index h if h of his or her Np papers have at least h citations each and the other (Np – h) papers have ≤h citations each” (Hirsch, Citation2005), whereby Np is the number of published papers of the scientist. In other words, it is the biggest number of documents published by an author that received at least that same number of citations.

4 A subfield of computer science which mostly studies the sentiment polarity and emotion in text. More information can be found in Liu (Citation2015).

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