Citation

Leeds DD, Zeng Y, Johnson BR, Foster CA, D'Lauro C. Brain Inj. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Informa - Taylor and Francis Group)

DOI

10.1080/02699052.2022.2034945

PMID

35133926

Abstract

BACKGROUND: Untreated concussions are an important health concern. The number of concussions sustained each year is difficult to pinpoint due to diverse reporting routes and many people not reporting. A growing body of literature investigates the motivations for concussion under-reporting, proposing ties with knowledge of concussion outcomes and concussion culture. The present work employs machine learning to identify trends in knowledge and willingness to self-report concussions.

METHODS: 2,204 cadets completed a survey addressing athletic and pilot status, concussion symptoms and outcome beliefs, ethical beliefs, demographics, and reporting willingness.

RESULTS: Clustering and non-negative matrix analysis identified connections to self-report willingness within: knowledge of symptoms, ethical beliefs, reporting requirements, and belief of long-term concussion outcomes. Support vector machine classification of cadet reporting likelihood reveals symptom and outcome knowledge may be inversely related to reporting among those rating ethics considerations as low, while heightened ethics may predict higher reporting likeliness overall.

CONCLUSIONS: Machine-learning analysis bolsters prior theories on the importance of concussion culture in reporting and indicate more symptom knowledge may decrease willingness to report. Uniquely, our analysis indicated importance of ethical behavior may be associated with general concussion reporting willingness, inviting further consideration from healthcare practitioners seeking increased reporting.

Language: en

Keywords

automated classification; concussion beliefs; Concussion reporting; factor analysis; reporting intentions