Latent profile analysis of operating room nurses’ occupational fatigue and its relationship with attentional control

This cross-sectional study was approved by the medical ethics committee. From May 2024 to July 2024, participants were selected from nine administrative areas in Chengdu via convenience sampling. The inclusion criteria were (a) registered nurses who worked in the operating room, (b) provided informed consent regarding their willingness to participate in the study and (c) had worked in this position for at least 1 year. The exclusion criteria were (a) nurses on shifts who were currently not in a hospital nursing position, (b) newly graduated registered nurses with standardized training, and (c) nurses who were not on duty for various reasons (such as further training, maternity leave or vacation, and health problems). In addition, questionnaires with incorrect entries, those with consecutive identical responses exhibiting a certain pattern, and those with more than 10% of items left unanswered will be excluded. Paper questionnaires were distributed both offline and online, and 386 operating room nurses from 6 tertiary hospitals in Chengdu were included.

For descriptive cross-sectional studies of quantitative variables, the sample size was calculated as follows [29] : \(N = {{{Z^2}\alpha /2p\left( {1 - p} \right)} \over {{\delta ^2}}}\)

At the 95% confidence interval, Z_α/2=1.96, δ represents the absolute error or precision, which was 0.05 in this study, and p is the incidence of severe occupational fatigue among nurses in the operating room that can be based on the data from previous research [30, 31], which was 26.3% here. According to the formula, the the oretical sample size was 298. Considering an invalid response rate of 20% during the study, it was concluded that at least 358 operating room nurses need to be investigated.

This study used a combination of offline and online methods to collect questionnaires. For nearby hospitals, onsite surveys were used to gather data. For distant hospitals, data collection was conducted using an online questionnaire administered via the Questionnaire Star platform (Wenjuanxing, http://​www.​wjx.​cn). Regardless of whether an offline or online survey was conducted, the researchers submitted ethical review approval documents and obtained the permission of the person in charge of the hospital before the investigation began. The investigators for this study comprised the research team, nursing departments in various hospitals and administrators of the hospital’s operating room department. All investigators received unified training and were responsible for selecting participants who met the inclusion criteria and informing the participants of the aim, significance, and content of the research. The research was anonymous. For offline questionnaires, after informed consent was obtained from the participants, the investigators introduced the requirements for completing the questionnaire to participants and then collected and reviewed the questionnaire on the spot. If any missing items were found, the respondent was invited to complete them in a timely manner. For the online questionnaires, the investigators sent the questionnaire link to the nursing WeChat group of each hospital and explained the purpose and completion requirements of the questionnaire survey to the participants through the WeChat group. After the participants provided informed consent, they could click the link to complete the questionnaire and submit it independently. To improve the quality of the online data collection, each IP address could be used only once to complete the questionnaire, and participants could submit the questionnaire when all options were completed. When the answers provided in a questionnaire were the same or the completion time for the online questionnaire was less than 300 s, the completed questionnaire was rejected.

Nurses’ occupational fatigue recovery was measured via the Occupational Fatigue Exhaustion Recovery scale (OFER) for operating room nurses, which was developed by WINWOOD et al. [31].and Fang et al. [32].translated the Chinese version of the Scale in 2009. The scale consists of 15 items in 3 subscales: chronic fatigue(items 1–5), acute fatigue(items 6–10) and intershift recovery(items 11–15) and a cumulative variance contribution of 70.367%. The instrument measures chronic fatigue, acute fatigue and intershift recovery with a 7-point Likert response scale ranging from 0 (never) to 6 (always). The scores of each subscale were converted to a percentage scale, calculated as the total score of the items divided by 30, multiplied by 100. The total score ranges from 0 to 100 points, and acute and chronic fatigue are divided into 3 levels (0 ~ 33.3 points indicates mild fatigue, 33.4 ~ 66.6 points indicates moderate fatigue, and 66.7 ~ 100.0 points indicates severe fatigue). The higher the scores of the acute and chronic fatigue subscales, the greater the degree of fatigue. The lower the score is for the intershift recovery subscale, the lower the degree of recovery between shifts. The scale in our study showed good internal consistency and reliability with a Cronbach’s alpha of 0.783. The Cronbach’s alpha coefficients for each of the three dimensions were 0.902 (chronic fatigue), 0.876 (acute fatigue), and 0.827 (intershift recovery).

The Attentional Control Scale (ACS) developed by Derryberry and Reed et al. [17].was originally developed in the form of a self-report survey. Yang et al. [33].translated the Chinese version of the Scale in 2014 and named it ACS-C. It measures the ability to control attention and contains 20 items on two subscales (i.e., 9 items on attentional focus and 11 items on attentional shift). This instrument measures attentional control on a 4-point Likert response scale ranging from 1 (never) to 4 (always). The higher the score, the better the individual’s attentional control ability is. The Cronbach’s alpha for attentional control was 0.801, and the Cronbach’s alpha coefficients for the dimensions of attentional focus and attentional shift were 0.874 and 0.776, respectively.

This research was approved by the medical ethics committee of Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China (approval no. 2024 − 197). All participants were informed of the purpose of the study before recruitment, and all participants were asked to voluntarily sign a written consent form. To protect the participants’ privacy, all collected data were preserved anonymously and confidentially.

Mplus version 8.0 was used to explore the latent profiles of operating room nurses’ occupational fatigue. Data for each item in the three dimensions were entered into the LPA. In this study, one to five potential profile models were explored sequentially from the initial model (1 profile) to the determination of the most appropriate model with a log-likelihood test. The LPA model fit test indices included the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the adjusted Bayesian information criterion (aBIC), with a lower value indicating a better-fitting model. The classification accuracy was evaluated with entropy values (from 0 to 1, with better values close to 1). The Lo–Mendell–Rubin Test (LMR) and bootstrap likelihood ratio test (BLRT) were used to assess the P values in the comparisons among models with different numbers of classes [34]. A low P value indicated that the k-class model fit better than the k-1-class model [35]. To explore the differences between demographic characteristics and work-related information for the subtypes based on LPA, IBM SPSS Statistics version 25.0 was used (IBM Corp., Armonk, NY, USA). Nurses’ demographic characteristics and work-related information, including the mean, standard deviation, frequency, and percentage, were analyzed via descriptive statistics. Differences between categorical variables of the different subtypes of operating room nurses’ occupational fatigue were analyzed via the chi-square test (χ2) or Fisher’s exact probability. Furthermore, multinomial logistic regression analysis was performed to investigate the predictive factors in the groups. One-way ANOVA, the Student–Newman–Keuls (SNK) test, and the Kruskal–Wallis test were conducted to determine differences in the ACS scores in each latent profile. Statistical significance was identified at a two-tailed P value < 0.05.

A total of 422 operating room nurses participated in the study. After 36 ineligible questionnaires (e.g.,14 the same response to all questions and more than 10% of the items in 22 questionnaires were not completed), 386 valid questionnaires were analyzed with an effective recovery rate of 91.4%. Most of the participants were female (84.2%), 53.1% were younger than 31 ~ 40 years, and 77.2% of the participants were married. The majority of the participants (82.1%) held a university degree, 53.1% of nurses had intermediate titles, and 33.9% had more than 13 years of experience in the operating room. More details can be found in Table 1.

To identify the best model, five models were estimated separately in this study. Table 2 displays the fit statistics of each model. As shown in Table 1, the entropy value of the 3-profile model was highest (0.934), and the AIC, BIC, and aBIC values continued to decrease and tended to stabilize after the 3-profile model. The LMR P values in the 4- and 5-profile models were not significant. Therefore, considering the proportion of each model and the practical significance of the results, we determined that the 3-profile model was the most appropriate model.

Table 3 shows the distribution of the three dimensions of occupational fatigue in the three-profile model, and Fig. 1 shows the scores of the three-profile model for the 15 items. Class 1 was the smallest group and accounted for 20.7% (n = 80) of all nurses. Operating room nurses in this class reported the lowest scores for chronic and acute fatigue but the highest score for intershift recovery. Therefore, this subgroup was labeled the “low-fatigue/high-recovery” group to indicate that operating room nurses in this group had a high tolerance for occupational fatigue and were able to recover from fatigue quickly, so their occupational fatigue was actually at a low level. Class 3 was the largest group and accounted for 50.0% (n = 193) of the participants. It was named the “moderate-fatigue/moderate-recovery” group to indicate that the nurses in this group had an average level of occupational fatigue. Finally, class 2 was the second largest group and accounted for 29.3% (n = 113) of the participants. Nurses in this group reported the highest scores for occupational fatigue. Moreover, the mean scores for the intershift recovery subdimension were lower than those in class 1 and class 3. Therefore, class 2 was named the “high-fatigue/low-recovery” group to indicate that operating room nurses in this group perceived severe fatigue and experienced a high level of occupational fatigue.

Pearson correlation analysis was used to analyze the relationships among the three dimensions of occupational fatigue and attentional control. Table 4 presents the results of the Pearson correlation analysis, which revealed that chronic fatigue and acute fatigue were negatively associated with attentional control, whereas intershift recovery had a positive association with attentional control.

Table 1 presents the results of the comparison of demographic and work-related characteristics across the three profiles. Significant differences were found between the groups in terms of age, work experience, education level, professional title, employment status, marital status, monthly income and average monthly number of night shifts (all P < 0.05).

To identify the demographic and work-related variables that affect occupational fatigue in operating room nurses across different profiles, a multinomial logistic regression analysis was used with the class 1 group as the reference. Profile membership was the outcome variable, and the predictor variables included age, education level, professional title, employment status, marital status, monthly income, and monthly average number of night shifts.

Table 5 presents the results of the factors that influenced latent profile membership. All predictors are highlighted in bold. Age, work experience, educational level and monthly income affected profile membership. In terms of age and monthly income, nurses aged 31–40 years were more likely to be placed in class 2 than nurses aged ≥ 41 years. Nurses whose monthly income was ≤ 5000 or 5000–10,000 were more likely to belong to class 2 than those whose income was >10,000. In terms of work experience, nurses with < 13 years of operating room work experience tended to be placed in class 2 more than nurses with ≥ 13 years of work experience. Nurses who had more than 13 years of work experience were more likely to belong to class 1. Furthermore, operating room nurses with master’s degrees were more likely to be in class 2 than those with college- and university-level education. Nurses who had less than 3 years of work experience were more likely to be in class 3. However, there were no major differences between class 1 and class 2 or class 3 in terms of professional title, employment status, marital status, and monthly average number of night shifts.

Table 6 shows the results of differences in the two dimensions of attentional control for the three profiles. The mean attentional focus scores of operating room nurses in classes 1, 2, and 3 were 35.9 (SD = 4.07), 28.8 (SD = 5.51), and 32.01 (SD = 3.86), respectively. The mean attentional shift scores of operating room nurses in classes 1, 2, and 3 were 21.27 (SD = 5.45), 20.92 (SD = 4.44), and 21.40 (SD = 3.60), respectively. The scores for attentional focus differed significantly across the three subgroups (P < 0.001), but the scores for attentional shift across the three subgroups did not differ significantly (P = 0.623). In addition, the SNK test results showed that the mean score for class 1 was significantly higher than the mean scores for class 2 and class 3.

The findings revealed that operating room nurses exhibited a significantly higher level of occupational fatigue compared to their counterparts in general wards, with a mean total score of (61.04 ± 9.97). Based on the established scoring criteria, this score indicates a moderately high level of occupational fatigue severity within this professional group. The elevated occupational fatigue among operating room nurses may be associated with multiple contributing factors, including but not limited to the specialized nature of surgical procedures, the high-pressure work environment, and the substantial psychological demands inherent in their professional responsibilities. Firstly, Operating room nurses have irregular working hours [36]. Surgeries are complex and require high levels of concentration and adaptability. The frequency of surgeries is high, resulting in significant work intensity, prolonged standing, and little to no breaks [10]. Secondly, the operating room environment is enclosed, and nurses frequently face challenges such as noise and chemical exposure; in contrast, ward nurses encounter different issues [8, 9]. Thirdly, Operating room nurses have limited contact with patients and their families, leading to simple communication and a tendency for their work value to be overlooked, resulting in a low sense of job value [37]. In contrast, ward nurses spend more time with patients, which facilitates the establishment of trust [38], allows for a more intuitive appreciation of their work value, and garners greater understanding and support. Therefore, the occupational fatigue of operating room nurses deserves greater attention.

This research aimed to analyze the differences in occupational fatigue among operating room nurses according to latent profiles. The findings of this research identified three distinct potential profiles of occupational fatigue for operating room nurses according to the score responses for each item, namely, “low-fatigue/high- recovery”, “high-fatigue/low-recovery” and “moderate-fatigue/moderate-recovery”. The findings suggest that occupational fatigue symptoms are heterogeneous among operating room nurses, similar to the findings of existing studies [39]. This categorization reflects the heterogeneity of occupational fatigue among operating room nurses in each latent profile. It complements previous studies that treat operating room nurses as a homogeneous whole and provides guidance for the development of targeted intervention measures to reduce nurses’ occupational fatigue.

The “low-fatigue/high-recovery” group consisted of 20.7% (n = 80) of the sample. The total mean score for occupational fatigue in this group was (48.66 ± 6.36). This population represents a small proportion of the study sample. Although occupational fatigue in this population is relatively low, it is present and should not be overlooked. Measures must be taken to prevent the escalation of fatigue levels in this group to more severe conditions. The low fatigue levels observed in this population may be attributed to their advanced age, robust family support, and strong self-regulation capabilities [40]. Understanding the reasons for these nurses’ rapid recovery from fatigue and the mechanisms involved (such as the effects of family support and self-regulation) can assist nurses in the other two groups who experience higher levels of fatigue. The “high-fatigue/low-recovery” group represented 29.3% (n = 113) of the sample. The total mean score for occupational fatigue in this group was (71.05 ± 6.54). The conditional probabilities of this group were greater for each factor, especially for the chronic and acute fatigue dimensions. Compared with the general population, this group of individuals exhibits significantly greater fatigue in relation to work, and their enthusiasm for work is nearly absent. Furthermore, nurses in the class 2 group exhibited significantly lower recovery rates from fatigue compared to the other two groups. It is advisable for nurses in this group to undergo timely psychological intervention to prevent the accumulation of negative emotions that could lead to the development of fatigue [41]. The “moderate-fatigue/moderate-recovery” group accounted for 50.0% (n = 193) of the sample with a total mean occupational fatigue score of (60.31 ± 5.86). This population exhibited moderate levels of both acute and chronic fatigue as well as a moderate capacity for recovery from fatigue. Their ability to recover from fatigue was well balanced with their occupational demands. More than three-quarters of the nurses fell into the class 2 and class 3 categories, suggesting that occupational fatigue among most operating room nurses in Chengdu is moderate to high with substantial potential for enhancement. To safeguard the stability of the nursing workforce, special attention should be given to these groups.

The demographic and work-related factors that influence profile membership include age, work experience, educational level and monthly income. In this study, nurses aged 31–40 were more likely to be assigned to the class 2 “high-fatigue/low-recovery” group than those older than 41 years, similar to the results of previous studies [32, 42]. This phenomenon may be attributed to the fact that operating room nurses within this age bracket constitute the optimal demographic for the workforce due to the demanding nature of their responsibilities and the physically taxing tasks they undertake. In terms of work experience, nurses with less than 13 years of work experience in the operating room also tended to be in the “high-fatigue and low- recovery” group. First, work experience significantly influenced the occupational fatigue of operating room nurses, consistent with the findings of previous research [32, 43]. Nurses with limited work experience predominantly serve as scrub nurses and expend more energy during surgical procedures than itinerant nurses do. Additionally, nurses with less experience often work longer hours and have shorter recovery periods between shifts. Due to their lack of experience, these nurses exhibit lower theoretical knowledge and emergency response capabilities, which may raise concerns about errors in intraoperative coordination. Consequently, they are likely to experience higher levels of occupational fatigue and to be classified as class 2. Moreover, nurses with a college education or below were more frequently observed in the “low- fatigue/high-recovery” group. Research has indicated that educational attainment is a significant predictor of occupational fatigue [44]. Operating room nurses with master’s degrees had lower scores for acute fatigue but higher scores for chronic fatigue [45]. Operating room nurses with master’s degrees, who often engage in management, research, and teaching, may experience heightened negative emotions and reduced work motivation due to their multifaceted responsibilities and stress, which may exacerbate chronic fatigue [22]. Hospitals typically regard master’s degree nurses as the core strength and future leaders of the nursing team. They are often assigned greater management and supervisory responsibilities, such as serving as nursing team leaders [46] or specialized nurses in management positions [47]. In these roles, they are responsible for overseeing and guiding the work of nurses within their teams, coordinating nursing resources, and ensuring the efficient and orderly execution of nursing tasks [48, 49]. Therefore, nurses with a master’s degree were more likely to be in the “high-fatigue/low-recovery” group. This study revealed that operating room nurses with lower monthly income were more likely to be classified into class 2, the “high-fatigue/low-recovery” group. The rationale for this phenomenon may be that as income increases, there is greater acknowledgement from the organization of the high level of effort exerted by these nurses. A fair income enables nurses to perceive that their contributions are recognized and rewarded [50], and the sense of satisfaction based on compensation and professional recognition also serves as a factor in alleviating occupational fatigue. Previous studies have demonstrated that addressing processing inefficiencies and enhancing personnel performance are effective strategies for mitigating fatigue [51].

Our study also revealed that the latent profile of operating room nurses’ occupational fatigue significantly influenced the attentional focus component of attentional control. This finding aligns with the results of previous research [23]. Operating room nurses who exhibit “low-fatigue/high-recovery” demonstrate superior attentional focus, whereas those in the “moderate-fatigue/moderate-recovery” and “high-fatigue/low- recovery” groups exhibit diminished attentional focus. This may be because operating room nurses who experience “low-fatigue/high-recovery” have more energy to consider the importance and meticulousness of surgical procedures. Research has demonstrated that individuals who experience chronic occupational fatigue frequently report challenges with concentration [52]. When operating room nurses experience fatigue, they might perceive their job as a series of tasks to be accomplished rather than carefully considering individual patients’ unique situations and their non-task needs during an operation [53]. Fatigued operating room nurses may encounter challenges in maintaining professional communication and may exhibit negative emotional responses. They may struggle to maintain a focus on their tasks. The unique demands of operating room work require nurses to invest significant energy to meet the requirements of surgical procedures. Given the rapid advancements in surgical techniques, nursing managers should prioritize addressing the issue of high occupational fatigue. Efforts should be made to mitigate fatigue in the context of consistently high-tension and high-intensity work environments to foster a shift toward a future in which nurses experience low fatigue and high recovery.

This research has several limitations. First, we used convenience sampling; thus, the representativeness of the sample and the generalizability of the study findings may have some limitations. Varied and stratified samples should be provided in further studies. An online questionnaire was used to recruit some of the participants and collect part of the data. The number of questionnaires distributed and the differences between nurses who participated and those who refused to participate are unknown. However, our research team conducted offline data collection as much as possible and collected online data as a supplement, and quality control was strictly maintained to maximize the scientific validity and credibility of the data. Second, the use of self-reported measures to assess occupational fatigue might have led to possible bias. Furthermore, we used attentional control to assess operating room nurses’ gathering and shifting of control. Although the ACS-C has been verified with other samples, it requires further verification in relation to the language and environment of hospital nurses. Third, this was a cross-sectional study, so the results of this research cannot be used to identify causality. Therefore, a causal relationship between operating room nurses’ occupational fatigue and attentional control cannot be determined. Further longitudinal studies are needed to track the trajectory of operating room nurses’ occupational fatigue over time.