Association of Shift Pattern and Shift Duration with Burnout among Healthcare Technologists Working in Emergency and Intensive Care Units: A Comparative Cross-Sectional Study

Demographic and Work-Related Characteristics of the Study Participants (N = 278)

Table 1 shows that most healthcare technologists were young, with 51.8% aged 18–25 years and 32.0% aged 26–35 years. Gender distribution was nearly equal (50.4% male, 49.6% female), and 52.2% were single. Participants were almost evenly distributed between emergency departments (47.1%) and ICUs (52.9%). Regarding workload, 35.3% worked ≤8 hours per shift, while 64.8% worked >8 hours per shift, including 30.6% working ≥12 hours per shift. In terms of work patterns, 30.9% had day/evening shifts, whereas 69.1% worked rotating or permanent night shifts.

Maslach Burnout Inventory-Human Services Survey

The study found different levels of burnout on the three dimensions (Figure 1). The majority (63.3%) of participants in EE expressed moderate burnout, with 30.6% expressing high burnout, and only 6.1% having low burnout. In DP, burnout was significantly higher: 74.1% of participants were in the high burnout category, 24.1% were in the moderate burnout category, and only 1.8% were in the low burnout category. Most participants (92.8%) had low personal accomplishment, indicating reduced professional fulfillment, whereas 5.4% and 1.8% of the participants comprised moderate and low burnout groups, respectively.

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Association of Shift Pattern and Shift Duration with Burnout Levels in Hospital Settings

Emotional Exhaustion (EE)

The analysis in Table 2 indicates that emotional exhaustion (EE) among healthcare technologists was not significantly influenced by department (emergency vs. ICU) or shift length, suggesting that working in high-acuity units or longer shifts alone did not determine EE levels. However, the shift pattern showed a significant association with EE (p = 0.003), with permanent night and rotating shifts exhibiting higher levels of emotional exhaustion compared to day/evening shifts. This suggests that non-traditional work schedules, particularly night and rotating shifts, may contribute more to emotional fatigue, likely due to circadian disruption, sleep disturbances, and increased work stress.

Depersonalization (DP)

Table 3 demonstrates DP association with work-related variables. DP levels differed significantly by department (p = 0.015), with ICU staff showing higher proportions of high DP (41.7%) compared to emergency department staff (32.4%).

DP was not significantly associated with shift length or shift pattern (p > 0.05 for both). Overall, ICU work appears to be the key factor linked to higher depersonalization among healthcare technologists, likely reflecting the intense and emotionally demanding nature of critical care environments.

Personal Accomplishment (PA)

The prevalence of PA scores across work-related variables was examined in Table 4. High levels of personal accomplishment were reported by the majority of healthcare technologists across departments, shift lengths, and shift patterns, with no significant differences observed (p > 0.05 for all). Specifically, 44.6% of emergency staff and 48.2% of ICU staff reported high PA, while moderate and low PA were rare. Similarly, shift duration and work schedule had minimal impact on PA. This suggests that, despite the demanding critical care environment and long or non-traditional shifts, most healthcare technologists maintain a strong sense of achievement and professional efficacy.

Correlation Matrix of Burnout Dimensions

Correlation Analysis: Department, Shift Length, Shift Pattern, Burnout Correlation In Table 5, correlation analysis revealed significant relationships between work-related factors and burnout dimensions. Department was significantly associated with shift length (p = 0.043) and DP (p = 0.037), indicating that ICU staff, compared to emergency staff, experienced longer shifts and higher DP. Shift length showed a significant positive correlation with EE (p = 0.028), suggesting that longer working hours contribute to higher EE. Similarly, shift pattern was significantly correlated with EE (p = 0.009), reflecting increased emotional exhaustion among staff on rotating or permanent night shifts. No significant correlations were observed between PA and any work-related factors, indicating that sense of professional achievement remains stable regardless of department, shift length, or shift pattern. Overall, these findings highlight that EE is particularly sensitive to shift-related factors, DP is influenced by departmental assignment, and PA is largely unaffected by work conditions.

Table 5: Spearman’s Correlation of Department, Shift Characteristics, and Burnout Dimensions (n = 278) Table 5: Spearman’s Correlation of Department, Shift Characteristics, and Burnout Dimensions (n = 278) Abbreviations: EE: Emotional Exhaustion, DP: Depersonalization, PA: Personal Accomplishment Predictors of Burnout Dimensions (Multiple Linear Regression) In Table 6, multiple linear regression analysis was performed to identify workplace factors predicting burnout dimensions among healthcare technologists (n = 278). For emotional exhaustion (EE), both shift length (B = 0.087, p = 0.034) and shift pattern (B = -0.104, p = 0.010) were significant predictors, indicating that longer shifts and non-traditional schedules contribute to higher EE. Depersonalization (DP) was significantly predicted by department (B = 0.136, p = 0.021), suggesting that ICU assignments increase feelings of detachment. In contrast, personal accomplishment (PA) was not significantly predicted by any workplace factor, reflecting that a sense of professional achievement remains stable regardless of department, shift length, or shift pattern. These findings emphasize that EE and DP are sensitive to work schedules and departmental assignments, whereas PA is resilient to workplace conditions.

Table 6: Multiple Linear Regression Analysis of Workplace Factors Predicting Burnout Dimensions (n = 278) Table 6: Multiple Linear Regression Analysis of Workplace Factors Predicting Burnout Dimensions (n = 278) Abbreviations: EE: Emotional Exhaustion, DP: Depersonalization, PA: Personal Accomplishment The study demonstrates high levels of burnout in ED and ICU healthcare technologists, with the majority expressed as emotional exhaustion (EE). According to recent studies, burnout estimates vary widely according to setting, profession, instrument, and threshold definition. ICUs have consistently reported higher rates due to their naturally stressful work environment [12, 13]. The high rate of burnout observed in this study emphasizes the critical need for targeted interventions in these high- acuity healthcare settings. The exceptional 69.4% burnout prevalence among participants in this study far surpasses international averages cited in current literature. Recent studies from comparable environments cite burnout prevalence between 32-40% among ICU healthcare workers, with the emotional exhaustion dimension being the most commonly impacted [14, 15]. The higher rates found in this study can be attributed to the cumulative effect of long-term exposure to high-stress work conditions, enhanced patient acuity, and the challenging nature of emergency and critical care environments. Studies highlighted that job stressors are more likely to cause burnout compared to demographics, and found their results to be consistent with the rates of emotional exhaustion outcomes [16, 17].

Shift length and shift pattern were both significantly associated with emotional exhaustion. This finding is especially troubling because shift work throws off the body’s rhythm and shortens recovery time for the next shift. Non-

day shift schedules were associated with greater emotional exhaustion (p = 0.003), which is consistent with the current literature that suggests working night shifts has additional physiological and psychological stressors [18]. This is supported by recent research, which found that nurses working 12-hour shifts experienced noticeably higher levels of burnout than those working 8-hour shifts. The regression test also confirmed that increased shift length (p = 0.034) and pattern of shifts (p = 0.010) predicted emotional exhaustion and emphasized the core practice of scheduling in burnout development [19].

Some literature suggests that early-career healthcare workers have fewer coping skills and lower professional resilience [20, 21]. The prospective association of experience may simply index the accumulation of effective coping strategies and mounting familiarity with the stresses of critical care areas over time. The findings of this study are compatible with research literature, which shows variations in burnout among healthcare professionals. Healthcare professionals are prone to having dual burdens of professional obligations and personal family tasks, and thus enhance stress and susceptibility toward burnout. The cultural and social demands to fulfill multiple roles can intensify the effects of work-related stressors and hence produce more severe symptoms of burnout [22]. No statistically significant association was observed for marital status in the present analyses. This was consistent with the buffering effect for stress management of social support networks found in a study [23].

While no significant differences emerged between ED and ICU technologists for emotional exhaustion and personal accomplishment, ICU staff members had significantly higher depersonalization (p = 0.015). This is a reflection of the unique psychosocial demands of ICU environments, in that prolonged accompaniment by severely ill patients, end-of- life experiences, and high mortality levels render emotional detachment a survival technique [14, 24]. ICU technologists’ higher depersonalization scores should be read as a potentially maladaptive coping response to sustained emotional demands for managing emotional labor in critical care.

Implications for Healthcare Organizations

These findings also have significant policy and practice implications for healthcare. The observed high level of burnout requires immediate organizational intervention in shift selection, workload management, and support systems at the psychosocial level. Evidence-based scheduling protocols, like limits for consecutive shifts and mandated rest breaks, should be instituted to neutralize the detrimental effects of long working hours [25, 26]. Hospitals also need to identify the unique vulnerability through special support initiatives and mentoring programs. Implementation of evidence-based mental health intervention programs, such as stress management courses, building resilience programs, and peer support groups, is a priority on the organizational agenda [10, 27].