Ordinary least squares regression-based difference-in-differences estimation approach for evaluating specialized nuclear emergency preparedness program

Cha, Seokki

doi:10.61186/ijrr.22.2.411

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Volume 22, Issue 2 (4-2024) Int J Radiat Res 2024, 22(2): 411-417 | Back to browse issues page

‎ 10.61186/ijrr.22.2.411

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Cha S. Ordinary least squares regression-based difference-in-differences estimation approach for evaluating specialized nuclear emergency preparedness program. Int J Radiat Res 2024; 22 (2) :411-417
URL: http://ijrr.com/article-1-5451-en.html

Ordinary least squares regression-based difference-in-differences estimation approach for evaluating specialized nuclear emergency preparedness program

Seokki Cha

Senior Researcher in R&D Strategy Center of Korea Institute of Science and Technology Information (KISTI), Seoul, South Korea , sc04@kisti.re.kr

Abstract: (1237 Views)

Background: This investigation elucidates the significance of radiation emergency medicine (REM) within South Korea, while addressing the multifaceted challenges linked to the education of medical personnel in the field of radiation emergency responses. The efficacy of REM training initiatives has undergone scrupulous evaluation through a variety of techniques, including, but not limited to, the application of the DISASTER Paradigm and engagement in simulation-based training exercises. Materials and Methods: The present research is structured to evaluate the incremental utility of REM training programs by applying the Difference-in-Difference (DID) estimation using OLS regression methodologies. Simultaneously, it aims to suggest potential improvements to existing training modules. Central to the methodology is the estimation of the DID model via the 'sm.ols' function in the Python programming environment. In the equation 'outcome ~ T_d + P_t + T_d * P_t', 'outcome' denotes the dependent variable under review, 'T_d' signifies the treatment dummy variable, and 'P_t' represents the period dummy variable. The interaction term 'T_d * P_t' elucidates the average effect of the treatment post-intervention, taking into account the temporal trend. Results: The conclusions drawn from this scholarly investigation have manifested negative net utilities across the three pivotal DISASTER Paradigm indicators (T, E, and R). Through the adept implementation of a Python-infused computational methodology, this study has yielded results characterized by precision and veracity. These insights furnish empirical evidence, indicating that the intervention in question may not have yielded an enhancement in the net utility for the designated target cohort. Conclusion: This scholarly inquiry underscores the efficacy and meticulous precision of OLS-DiD estimations executed via a Python-centric computational approach. The empirical findings emanating from this research serve to fortify a robust foundation for the strategic navigation of unique challenges within the intersecting realms of nuclear science and medical studies, with particular emphasis on advancing the field of radiation emergency medicine (REM) education.

Keywords: Radiation emergency medicine, training programs, difference-in-difference analysis, computational social science.

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Type of Study: Original Research | Subject: Radiation Biology

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