Journal of Educational Evaluation for Health Professions

Kyle Covington

3 Articles

Analysis of the Clinical Education Situation framework: a tool for identifying the root cause of student failure in the United States: Katherine Myers, Kyle Covington; J Educ Eval Health Prof. 2019;16:11. Published online May 10, 2019; DOI: https://doi.org/10.3352/jeehp.2019.16.11

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Doctor of physical therapy preparation requires extensive time in precepted clinical education, which involves multiple stakeholders. Student outcomes in clinical education are impacted by many factors, and, in the case of failure, it can be challenging to determine which factors played a primary role in the poor result. Using existing root-cause analysis processes, the authors developed and implemented a framework designed to identify the causes of student failure in clinical education. This framework, when applied to a specific student failure event, can be used to identify the factors that contributed to the situation and to reveal opportunities for improvement in both the clinical and academic environments. A root-cause analysis framework can help to drive change at the programmatic level, and future studies should focus on the framework’s application to a variety of clinical and didactic settings.

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Applying the 2022 Cardiovascular and Pulmonary Entry-Level Physical Therapist Competencies to Physical Therapist Education and Practice
Nancy Smith, Angela Campbell, Morgan Johanson, Pamela Bartlo, Naomi Bauer, Sagan Everett
Journal of Physical Therapy Education.2023; 37(3): 165. CrossRef
Cardiovascular and Pulmonary Entry-Level Physical Therapist Competencies: Update by Academy of Cardiovascular & Pulmonary Physical Therapy Task Force
Morgan Johanson, Pamela Bartlo, Naomi Bauer, Angela Campbell, Sagan Everett, Nancy Smith
Cardiopulmonary Physical Therapy Journal.2023; 34(4): 183. CrossRef
The situational analysis of teaching-learning in clinical education in Iran: a postmodern grounded theory study
Soleiman Ahmady, Hamed Khani
BMC Medical Education.2022;[Epub] CrossRef

A novel tool for evaluating non-cognitive traits of doctor of physical therapy learners in the United States: Marcus Roll, Lara Canham, Paul Salamh, Kyle Covington, Corey Simon, Chad Cook; J Educ Eval Health Prof. 2018;15:19. Published online August 17, 2018; DOI: https://doi.org/10.3352/jeehp.2018.15.19

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Abstract PDF Supplementary Material

Purpose
The primary aim of this study was to develop a survey addressing an individual’s non-cognitive traits, such as emotional intelligence, interpersonal skills, social intelligence, psychological flexibility, and grit. Such a tool would provide beneficial information for the continued development of admissions standards and would help better capture the full breadth of experience and capabilities of applicants applying to doctor of physical therapy (DPT) programs.
Methods
This was a cross-sectional survey study involving learners in DPT programs at 3 academic institutions in the United States. A survey was developed based on established non-proprietary, non-cognitive measures affiliated with success and resilience. The survey was assessed for face validity, and exploratory factor analysis (EFA) was used to identify subgroups of factors based on responses to the items.
Results
A total of 298 participants (90.3%) completed all elements of the survey. EFA yielded 39 items for dimensional assessment with regression coefficients < 0.4. Within the 39 items, 3 latent constructs were identified: adaptability (16 items), intuitiveness (12 items), and engagement (11 items).
Conclusion
This preliminary non-cognitive assessment survey will be able to play a valuable role in DPT admissions decisions following further examination and refinement.

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A Systematic Review of Variables Used in Physical Therapist Education Program Admissions Part 2: Noncognitive Variables
Andrea N. Bowens
Journal of Physical Therapy Education.2024; 38(3): 192. CrossRef
An exploration of the relationship between grit, reflection-in-learning, and academic performance in entry-level doctor of physical therapy students
Elizabeth M Ardolino, Hazel Anderson, Katherine F Wilford
Physiotherapy Theory and Practice.2024; : 1. CrossRef
Predictors of Success on the National Physical Therapy Examination in 2 US Accelerated-Hybrid Doctor of Physical Therapy Programs
Breanna Reynolds, Casey Unverzagt, Alex Koszalinski, Roberta Gatlin, Jill Seale, Kendra Gagnon, Kareaion Eaton, Shane L. Koppenhaver
Journal of Physical Therapy Education.2022; 36(3): 225. CrossRef
Grit, Resilience, Mindset, and Academic Success in Physical Therapist Students: A Cross-Sectional, Multicenter Study
Marlena Calo, Belinda Judd, Lucy Chipchase, Felicity Blackstock, Casey L Peiris
Physical Therapy.2022;[Epub] CrossRef
Predicting graduate student performance – A case study
Jinghua Nie, Ashrafee Hossain
Journal of Further and Higher Education.2021; 45(4): 524. CrossRef
Examining Demographic and Preadmission Factors Predictive of First Year and Overall Program Success in a Public Physical Therapist Education Program
Katy Mitchell, Jennifer Ellison, Elke Schaumberg, Peggy Gleeson, Christina Bickley, Anna Naiki, Severin Travis
Journal of Physical Therapy Education.2021; 35(3): 203. CrossRef
Doctor of Physical Therapy Student Grit as a Predictor of Academic Success: A Pilot Study
Rebecca Bliss, Erin Jacobson
Health Professions Education.2020; 6(4): 522. CrossRef
Personality-oriented job analysis to identify non-cognitive factors predictive of performance in a doctor of physical therapy program in the United States
Maureen Conard, Kristin Schweizer
Journal of Educational Evaluation for Health Professions.2018; 15: 34. CrossRef

Selectivity of physiotherapist programs in the United States does not differ by institutional funding source or research activity level: Sean P. Riley, Kyle Covington, Michel D. Landry, Christine McCallum, Chalee Engelhard, Chad E. Cook; J Educ Eval Health Prof. 2016;13:17. Published online April 15, 2016; DOI: https://doi.org/10.3352/jeehp.2016.13.17

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Abstract PDF

Purpose
This study aimed to compare selectivity characteristics among institution characteristics to determine differences by institutional funding source (public vs. private) or research activity level (research vs. non-research). Methods: This study included information provided by the Commission on Accreditation in Physical Therapy Education (CAPTE) and the Federation of State Boards of Physical Therapy. Data were extracted from all students who graduated in 2011 from accredited physical therapy programs in the United States. The public and private designations of the institutions were extracted directly from the classifications from the ‘CAPTE annual accreditation report,’ and high and low research activity was determined based on Carnegie classifications. The institutions were classified into four groups: public/research intensive, public/non-research intensive, private/research intensive, and private/non-research intensive. Descriptive and comparison analyses with post hoc testing were performed to determine whether there were statistically significant differences among the four groups. Results: Although there were statistically significant baseline grade point average differences among the four categorized groups, there were no significant differences in licensure pass rates or for any of the selectivity variables of interest. Conclusion: Selectivity characteristics did not differ by institutional funding source (public vs. private) or research activity level (research vs. non-research). This suggests that the concerns about reduced selectivity among physiotherapy programs, specifically the types that are experiencing the largest proliferation, appear less warranted.

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Predictors of research productivity among physical therapy programs in the United States: an observational study
David M. Rowland, Amanda A. Murphy, Hannah R. Manik, Chris Y. Lane, Deborah L. Givens, Chad E. Cook, Alessandra Narciso Garcia
BMC Medical Education.2020;[Epub] CrossRef
Impact of funding allocation on physical therapist research productivity and DPT student graduates: an analysis using panel data
Tara Dickson, P. Daniel Chen, Barrett Taylor
Advances in Health Sciences Education.2019; 24(2): 269. CrossRef
Predicting performance in health professions education programs from admissions information – Comparisons of other health professions with pharmacy
Richard E. Wilcox, Kenneth A. Lawson
Currents in Pharmacy Teaching and Learning.2018; 10(4): 529. CrossRef
20th Pauline Cerasoli Lecture: The Sunk Cost Fallacy
Chad Cook
Journal of Physical Therapy Education.2017; 31(3): 10. CrossRef

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