title:
An exploration of modifiable programmatic factors as Predictors of Success on the NREMT Paramedic Computer Adaptive Text: A Predictive Model
creator:
Lancaster, Scott
subject:
Dissertations, Academic.
description:
Background: It is not known why the National Registry of Emergency Medical Technicians (NREMT) paramedic exam pass rates fall below other allied health programs or what aspects ofparamedic programs may predict success on the NREMT examination. One reason may be the lack of standardization of program design. Paramedic educational models vary greatly, not only across the country, but also even within states. While the Emergency Medical Services (EMS) Educational Standards have been promoted for years setting the backbone of the paramedic curriculum, there is little standardization from program toprogram in model, curriculum design, hours of instruction, clinical requirements, science course requirements, and the use of entrance and exit exams.Purpose: The average first-attempt national pass rate on the NREMT paramedic computer adaptive test (CAT) was 74% (median= 75%) for the year ending December 31, 2017 (NREMT, 2018a), although there is a wide range of pass rates across programs and states. Thereasons for this low rate are largely unknown. This study examined modifiable programmatic factors to determine which, if any, predict students’ first-attempt success on the NREMT examination. Methods: An invitation to participate in a cross-sectional electronic survey study was sent by email to all 585 accredited paramedic program directors across the United States. The survey asked for region and school classification as well as modifiable programmatic factors such as pedagogical approaches, instructor staffing, duration of program, and course prerequisites. Respondents were also asked to provide self-reported 2017 first time pass rates on the NREMT for their respective programs.Results: A total of 278 program directors completed this survey. The distribution of first time pass rates was skewed toward relatively higher pass rates 86.5% (mean=83.0, SD=15.4, minimum=28, maximum=100) when compared to the national mean of 74%. A median split was used to create a binary variable for analysis (lower vs. higher pass rate). Univariate and multivariate logistic regression models were created to predict the sample median first time pass rate from 20 predictor variables. The final model predicting the sample median pass rate included the use of flipped classroom techniques, computer based testing (CBT), and a greater number of field hours. Programs with flipped classrooms had almost twice the odds of having higher pass rates than programs that did not utilize this technique. Each additional field hour associated with a 1.002 increased odds of being classified as a high pass rate program, and programs that utilize CBT have a decrease in the odds of being classified as a high pass rate program. The model based on the sample predicted 8.7% of the variability in pass rates. For comparison purposes, additional regression models were built to predict pass rates based on dichotomizing the sample using the 2017 national median. The final national median predictive model included different factors: program delivery model and minimum exit exam score. This model predicted 7% of the variability in pass rates.Conclusions: This study was the first to examine several modifiable programmatic predictors of success for paramedic programs in the United States. The amount of variability in pass rates accounted for by programmatic factors was much lower than models in the literature using individual student level demographic characteristics, but were nonetheless important to identify. This study begins to fill a gap in the literature concerning educational best practices and predictive models of success in this field.
publisher:
Simmons University (Boston, Mass.)
contributor:
date:
2019
type:
Text
format:
1 PDF (111 Pages)
identifier:
td_hpe_2019_sl
source:
language:
English
relation:
coverage:
rights:
Material from the Simmons University Archives collections are made available for study purposes only. For more information, or to request rights to reproduce or reuse any material, contact the University Archives at archives@simmons.edu.
