Back

Different perspectives on Quality in clinical education of physical therapy students - protocol of a scoping review

Walter, M. M.; Rogan, S.; Schurz, A. P.; Zinzen, E.

2023-10-21 health systems and quality improvement
10.1101/2023.10.20.23297304 medRxiv
Show abstract

BackgroundClinical education (CE) plays a crucial role in physical therapy education, yet there is a notable absence of established quality characteristics for its implementation. ObjectivesThis scoping review aims to elucidate how various stakeholders define and describe the quality of CE in higher education for physical therapy students. Additionally, it seeks to identify commonalities and distinctions in the application of the term "quality" in the context of CE. MethodsPeer-reviewed studies encompassing physical therapy students, clinical instructors, lecturers in physical therapy education, physical therapy educational sites, and supervising physical therapists in internships across all clinical fields will be included in the review. A comprehensive search strategy will be employed, utilizing multiple electronic databases, including MEDLINE, EMBASE, the Cochrane Library, ERIC, Education Research Complete, Education Database, and CINAHL. Eligibility screening will be independently conducted by two reviewers. Data extraction will be presented in a tabular or graphical format, aligning with the reviews objectives. DiscussionInsights gleaned from this study hold the potential to inform targeted interventions and improvements in CE, ultimately enhancing the learning outcomes and satisfaction of physical therapy students. This endeavor seeks to bridge the existing gap in defining and achieving quality in clinical education within the realm of physical therapy higher education.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.