Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Workplace injuries impose substantial human and organizational costs, with scheduling risks such as overtime, fatigue accumulation, and irregular work demands frequently linked to increased injury rates. Day-off call-ins, unscheduled work assignments requiring employees to report to work on a scheduled day off, remain an understudied source of schedule risk. This study investigates whether day-off call-ins are associated with workplace incident occurrence using 2022–2024 payroll and safety data from a large oil refinery. Using unit-level logistic regression analyses, incident occurrence (0 = no incident, 1 = incident), will be predicted from call-in staffing levels while controlling for unit, turnaround status, production levels, day of the week, holidays, and regular staffing levels. Call-in staffing levels are expected to positively predict workplace incidents, demonstrating that day-off call-ins present a distinct schedule risk beyond standard staffing levels. This research's findings could suggest organizations to monitor call-in frequency as a leading safety indicator and integrate call-in metrics into fatigue risk management and workforce planning.
Date
November 2026
Subject
Industrial and organizational psychology
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Charlie's poster
Included in
Answering the call: examining the relationship between call-In work and occupational injury risk
Workplace injuries impose substantial human and organizational costs, with scheduling risks such as overtime, fatigue accumulation, and irregular work demands frequently linked to increased injury rates. Day-off call-ins, unscheduled work assignments requiring employees to report to work on a scheduled day off, remain an understudied source of schedule risk. This study investigates whether day-off call-ins are associated with workplace incident occurrence using 2022–2024 payroll and safety data from a large oil refinery. Using unit-level logistic regression analyses, incident occurrence (0 = no incident, 1 = incident), will be predicted from call-in staffing levels while controlling for unit, turnaround status, production levels, day of the week, holidays, and regular staffing levels. Call-in staffing levels are expected to positively predict workplace incidents, demonstrating that day-off call-ins present a distinct schedule risk beyond standard staffing levels. This research's findings could suggest organizations to monitor call-in frequency as a leading safety indicator and integrate call-in metrics into fatigue risk management and workforce planning.
Department
University of Tennessee at Chattanooga. Dept. of Psychology