Identifying Determinants of Performance for Females Completing a Paramedic Physical Employment Test

Abstract

Background: Sex disparities exist in employment and injury rates in the paramedic sector. Low success rates among females attempting physical employment standards could explain the elevated injury risk among female paramedics. Identifying factors that underpin successful work-related performance can inform pre-hire and return-to-work based physical training programs to address these disparities.

Purpose: The purpose of this thesis was to identify the determinants of successful physical performance for females engaged in paramedic tasks.

Research Question 1: Participant demographics, college type, employment status and heart rate were obtained from female participants who completed the Ottawa Paramedic Physical Abilities Test (OPPAT), a physical employment standard for paramedics. These data were used in a logistic regression model to determine which factors could predict the likelihood of successfully completing the OPPAT. Females who were actively employed, who were educated in a public paramedic college, who had higher body mass, or those who had lower BMI were more likely to successfully complete the OPPAT.

Research Question 2: Lift duration and the time between peak knee and hip joint angular velocity during the Scoop and Barbell lift were compared between females who passed and failed the Ottawa Paramedic Physical Abilities Test. Four ANCOVAs were used for these comparisons where college type (public or private) and employment status (employed or unemployed) were used as categorical factors and body mass and BMI were used as covariates. No significant differences were found between passing and failing females.

Discussion: Modulating demographic factors that increase the likelihood of success could lead to improved performance outcomes, but other determinants should be explored to improve the predictive ability of the current model. Future research should continue to leverage emerging technology, such as markerless motion capture and unsupervised machine learning, to identify determinants of success for females in paramedic tasks.