Study of Nursing Workload Using the Nursing Activities Score in Level 2 and Level 3 ICUs in Latvia: A 3-Month Observation

Study of Nursing Workload Using the Nursing Activities Score in Level 2 and Level 3 ICUs in Latvia: A 3-Month Observation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study of Nursing Workload Using the Nursing Activities Score in Level 2 and Level 3 ICUs in Latvia: A 3-Month Observation Olga Cerela-Boltunova, Inga Millere This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7516003/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in BMC Nursing → Version 1 posted 15 You are reading this latest preprint version Abstract This study presents a three-month prospective observational analysis of nursing workload in Intensive Care Units (ICUs) in Latvia using the Nursing Activities Score (NAS). The objective was to implement the NAS instrument in three hospitals representing different levels of care (level II and III) and to assess inter-institutional differences in workload. Data were collected from 3420 nursing care episodes across day and night shifts between February and May 2025. Descriptive and inferential statistics revealed statistically significant differences in NAS scores between the hospitals, with the highest workload observed in the level III facility. No significant difference was found between day and night shifts in overall NAS per nurse, but a significantly higher nurse shortage was identified during night shifts. Strong correlations were observed between NAS total scores, required nursing staff, and actual staffing levels, highlighting critical disparities. Several extreme overload cases were identified, exceeding the safe workload threshold of 100 NAS points per nurse per shift. The findings underscore the need for structured NAS-based workforce planning in Latvian ICUs, particularly in higher-level hospitals. The study demonstrates the NAS instrument's practical applicability for workload monitoring and suggests that its broader implementation can enhance patient safety and staff well-being. workload intensive care units Latvia health workforce Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Intensive care units (ICUs) are highly specialised structural units where critically ill patients are provided with continuous monitoring and support of vital functions [ 1 ]. The Nursing Activities Score (NAS) instrument is widely used worldwide to objectively quantify intensive care nursing workload. This instrument was developed in 2003 by Miranda et al. as a modified version of the TISS-28 (Therapeutic Intervention Scoring System) [ 2 ]. Compared to the traditional TISS-28, the NAS has been supplemented with five new aspects of care: patient monitoring and parametric adjustment, hygiene and mobilisation, care of family members and administrative tasks [ 3 ]. For each of these tasks, a time weighting value is calculated and the sum of these values reflects the actual working time of the nurses [ 2 ]. In international studies, the NAS has proven to be an accurate workload measurement instrument and is able to cover approximately 81% of nurses’ working time capacity, while the TISS-28 only covers approximately 43% [ 4 ]. The NAS score has been translated into many languages and implemented in at least 12 countries, including Norway [ 5 ], Spain [ 6 ] and Brazil [ 3 ]. Inter-national studies [ 7 ] have shown considerable variability, with the mean NAS value in intensive care units in seven countries being around 72.8%, with a range from 44.5% (Spain) [ 6 ] to 101.8% (Norway) [ 6 ]. This demonstrates the adaptability of the NAS instrument to different healthcare system settings and its usefulness for comparing nursing workload across ICUs. In Latvia, the levels of intensive care units and the organisation of their activities are laid down in laws and regulations. Cabinet of Ministers Regulation No. 60 of 2009 on medical treatment institutions and their structural units states that patients in ICUs are under the continuous care and supervision of physicians and nursing staff, receive intensive treatment and support of vital functions [ 8 ]. The regulation emphasises that the basic unit of each ICU bed is one patient [ 8 ]. In practical healthcare, Latvian ICUs are divided into three levels: level 1 (low intensity), level 2 (medium intensity) and level 3 (highly specialised) units [ 9 ]. This hierarchical structure reflects the profile of the Latvian healthcare system, with the main university hospitals providing the most specialised intensive care, and the regional ones providing the basic level of care. The availability of nurses is one of the most pressing challenges in the context of intensive care, and this problem is particularly pronounced in Latvia [ 10 ]. International organisations predict a shortage of nurses in many countries, especially in those with rapidly ageing populations [ 11 ]. In Latvia, the trend is alarming, with only 418 registered nurses per 100,000 population in 2020 (around 419 in 2021) [ 12 ], while the European Union average was around 770–850 in 2021, and this figure continues to lag significantly behind [ 13 – 14 ]. ICU leadership and hospital administration experience this deficit most acutely. Although in theory the nurse-to-patient ratio is determined by the level of care, and a ratio of 1:3–4 is recommend for ICUs [ 15 ], these recommendations are not legally binding and have not been integrated into the national regulatory framework. The risk of burnout among nurses is increasing, and many Latvian nurses are choosing to work abroad, where there is a demand for ICU nurses, better working conditions, and more competitive salaries [ 16 ]. To counter these trends, the state needs to ensure sustainable remuneration and an improved working environment to motivate existing staff and attract the next generation. Nursing workload is a concept that describes the time and effort that the nursing team has to invest in patient care [ 17 ]. This workload consists of both direct care tasks, such as dispensing medication and monitoring the patient, and indirect activities, such as documentation and communication with the patient’s family [ 18 ]. Excessive workload can lead to professional burnout, emotional exhaustion, and lower job satisfaction [ 19 ], which in turn contributes to staff turnover and further shortages in healthcare [ 20 ]. For patients, this means an increased risk of medical errors, delayed procedures, and more frequent complications [ 21 ]. Insufficient workload, in its turn, can signal inefficient use of resources [ 22 ]. Healthcare managers need objective information about actual resource consumption to make informed organisational decisions [ 23 ]. This is particularly important in intensive care, where patient conditions are severe and care is continuous. Experts emphasise that in intensive care, the workload does not depend only on the severity of the patient’s illness, but also on the unit’s infrastructure, team organisation, technological support, and other systemic aspects [ 24 ]. Accurate work-load measurement in such circumstances helps to ensure efficient staff distribution, increase patient safety, and protect the health of employees in the long term [ 25 ]. In view of the above, the focus of this study is on the implementation of the NAS and the analysis of its practical application in Latvian intensive care units. The aim of the study is to implement the NAS in the ICUs of three hospitals of different levels (designated A, B, and C), analysing a three-month period from 6 February to 7 May 2025. During this period, data on nurses’ activities was systematically collected using the NAS as an instrument to quantify workload. 2. Methods 2.1. Design and Aim of the Study This study was designed as a prospective, observational study to assess the nursing workload in intensive care units in Latvia using the NAS. The main objective of the study was to identify the actual intensity of care and to identify inter-institutional differences, as well as to provide evidence-based conclusions on the level of staff workload. Data was collected from three Latvian medical treatment institutions with ICUs, representing different levels of care and clinical experience. Hospital A – a university-type institution of level 3 with highly specialised equipment and a multidisciplinary team. Hospital B and Hospital C – regional medical treatment institutions of level 2 with different infrastructure and human resources. All participating ICUs provide care for adult patients with life-threatening conditions. The most common patient diagnoses across these ICUs were sepsis, postoperative monitoring after major surgery and respiratory failure requiring mechanical ventilation. In addition to registered nurses, ICU teams included nurse assistants who supported basic care and logistical tasks. All admitted patients were included in the analysis, including those with short ICU stays of less than 24 hours. The study used a purposive sample, which was formed by contacting several medical treatment institutions in various regions of Latvia. The inclusion criteria for the sample were that the NAS is used in the intensive care units or there is a possibility of adapting it to the healthcare documentation, structured and regularly completed documentation of patient care processes is available, and it is possible to ensure data anonymisation and processing in accordance with ethical requirements. The total study sample consisted of n = 3420 patient care episodes recorded over a defined time period, including both day and night shifts, thus providing a comprehensive assessment of workload over a 12- and 24-hour period. 2.2. Data Collection Process Data collection took place between 06.02.2025 and 07.05.2025, in collaboration with local staff (nurses) in each medical treatment institution. The data was collected using the NAS and recorded on a dedicated digital data entry platform that was developed to facilitate uniform and standardised data entry. During each 12-hour shift, several nurses completed the NAS about their patients based on real-time care or retrospectively documented information in patient care protocols. Additional structured data was also collected on type of medical treatment institution (university/regional/private), number of intensive care beds, staff ratio (nurse-to-patient ratio), and shift type (day/night) and date. The NAS was selected as the only instrument because it is an internationally validated and comparable tool for quantifying ICU nursing workload. However, it does not capture broader contextual factors (e.g., referral system, nurse competences, or technological resources), which must be considered when interpreting the results. 2.3. Instrument Used The NAS is a validated, internationally used instrument that allows for the quantitative assessment of nursing workload in the care of intensive care patients. The score was developed in 2003 by Miranda et al. [ 2 ], based on a modification of the TISS-28 and adapted to include non-invasive but important care activities such as support of the patient’s family and documentation. The NAS consists of 23 individual care elements covering different aspects of clinical care. Each item is evaluated according to a certain value (for example, 1.a – 4.5 points, 2–3.5 points, etc.). It consists of items covering care activities, ventilatory, cardiovascular, renal, neurological and metabolic support, as well as specific interventions such as surgical procedures, diagnostic tests, and patient transportation. Each item has a predefined weight, and the total score ranges from 0 to 177 points, corresponding to the percentage of time a nurse spends caring for one patient during a shift. A score of 100 points is equivalent to the workload of one nurse per shift. A score above 100 indicates that a patient requires the equivalent of more than one full-time nurse. In this study, NAS was completed for every patient in each 12-hour shift. The total NAS points per shift were calculated and divided by 100 to estimate the required number of nurses. All admitted patients were included, including those with short ICU stays of less than 24 hours. No demographic data about the nurses (e.g., sex, years of experience) were collected, as the focus of the study was exclusively on patient-related workload episodes. The number of points corresponds to the required time investment by the staff – 100 points correspond to 12 hours of work by one nurse. The score can be used as an assessment of a 12-hour or 24-hour work shift. In Latvia, the NAS was adapted a little earlier and validated in the conditions of one unit. Cerela-Boltunova et al, in 2022 [ 26 ], provided a translation into Latvian, which experts rejected and clarified individual items, subsequently piloting the score in an intensive care unit (42 patients, NAS completed 226 times). They reported very good reliability of the score (Cronbach’s alpha 0.973) and CVI = 0.909 of the final version, confirming the suitability of the NAS for the Latvian environment. However, until now, there was a lack of research comparing NAS results between different intensive care units in Latvia or analysing the usability of this score in practice. 2.4. Data Entry and Processing After the data was collected using the NAS, the results were automatically exported from the dedicated platform in a structured Excel format. The data sets were checked and prepared for further statistical processing using IBM SPSS Statistics 28.0 software. Statistical analyses were performed by the first author, who has advanced training in SPSS, in consultation with a certified statistician to ensure methodological accuracy. First, initial data cleaning was performed, including missing value checking and logical error correction, as well as data adequacy validation for each variable. For each episode of care, the total NAS score in a 12-hour shift was calculated. As the hospitals included in the study represented different levels of care, special attention was paid to the analysis of inter-institutional differences. 2.5. Statistical analysis Descriptive statistics included frequency distributions, mean, SD, median, as well as min and max values. Visual representations, including histograms, boxplots, and bar graphs, were created to analyse the distribution and possible asymmetry of the data. For further analysis, the normality of data distribution was tested using Kolmogorov-Smirnov and Shapiro-Wilk tests. The most appropriate inferential statistical tests were selected according to the results. One-way analysis of variance (ANOVA) technique was used to compare NAS means between hospitals if the data distribution was close to normal. If the data distribution did not meet the normality assumptions, the non-parametric Kruskal-Wallis H test was used to identify statistically significant differences between hospitals. Mann-Whitney U tests were performed for additional analyses between two hospitals. The results of the analysis showed that the mean NAS score in Hospital A (level 3 care) was significantly higher than in Hospitals B and C (level 2), and this difference was statistically significant (Kruskal-Wallis, p < 0.001). In addition, linear regression analysis was conducted to evaluate the relationship between the NAS per nurse and the required number of nurses. Regression coefficients, intercept, significance values, and the coefficient of determination (R²) were calculated to assess the strength and direction of the relationship. The regression line and equation were obtained from SPSS version 28.0 output, with statistical significance set at p < 0.05. 2.6. Ethical Consideration The study was conducted in compliance with the ethical principles [ 27 ] and Latvian laws and regulations [ 8 ]. The ethics committees of all three participating hospitals approved the project prior to the data collection. No additional interventions were made in the treatment of patients during the study. Completion of the NAS questionnaire was part of the clinical practice monitoring. All questionnaires were anonymised and no personal or specific health data of patients were collected that could reveal individual information. The nurses’ participation in the study was voluntary and it was explained to them in advance that the results would be analysed only in collective summaries. They thus had no additional trust concerns regarding their own privacy and that of their patients. Confidentiality was ensured in data processing and storage in accordance with the Ministry of Welfare guidelines on patient and staff protection [ 28 ]. Ethical approval for the study was obtained from the Ethics Committee of Riga Stradiņš University (Decision No. 2-PĒK-4/416/2023, 09 May 2023). 3. Results A total of 3420 completed NAS protocols were obtained in the study from three Latvian ICUs representing different levels of care. The data collection took place over a period of three months (6 February to 7 May 2025), covering both day and night 12-hour shifts. Table No. 1 presents basic information on the number of completed protocols, average NAS scores, SD, medians, as well as min and max values in each hospital and by shift. It provides an overview of the intensity and distribution of nursing workload across the three participating medical treatment institutions. Table 1 NAS score distribution and descriptive statistics for different hospitals and shifts. Indicator Hospital Total A B C Number of protocols 2491 402 527 3420 Number of protocols (day) 1542 239 314 2095 Number of protocols (night) 949 163 213 1325 Mean (day) 58,38 106,96 71,31 65,87 SD (day) 20,14 18,83 24,61 25,84 Number of nurses (day) 8 2 2 - Mean (night) 58,13 105,18 70,85 65,96 SD (night) 19,20 18,99 19,69 25,84 Number of nurses (night) 8 2 2 - Standard Error 0,396 0,942 0,989 - 95% CI Lower Bound 57,51 104,38 69,18 - 95% CI Upper Bound 59,06 108,09 73,07 - Skewness 0,652 0,351 0,111 - Kurtosis 0,263 0,293 0,281 - Median (total) 55,00 103,30 71,60 - Total Mean (overall) 58,39 106,24 71,12 65,91 Total SD (overall) 19,78 18,89 22,73 25,28 Total Mean (day) 65,87 - Total SD (day) 25,84 - Total Mena (night) 65,96 - Total SD (night) 25,84 - Min NAS points per 1 nurse 21,40 33,30 25,80 - Max NAS points per 1 nurse 136,50 156,90 154,30 - Min NAS points per 12-hour shift 25,80 54,40 41,50 - Max NAS points per 12-hour shift 2620,80 846,70 764,20 - Figure No. 1 illustrates the workload dynamics in different hospitals and allows the identification of peak workload days. Hospital B consistently shows a higher NAS score than Hospitals A and C. To assess statistically significant differences in nursing workload between the three ICUs (Hospital A, B, and C), an analysis of variance (One-Way ANOVA) was performed using the sum of NAS scores as the dependent variable. The results show that there are statistically significant differences between hospitals in the NAS score levels (F(2, 3417) = 954.415, p < 0.001 and Partial Eta Squared = 0.359), indicating a significant effect of the hospital factor on nursing workload. Factors such as “Shift” are not statistically significant (F = 0.764; p = 0.382) and the effect size is also insignificant (Partial Eta Squared = 0.000). So there is no significant difference in the NAS score between day and night shifts. The interaction between hospital and shift is also not significant (F = 0.233; p = 0.792; Partial Eta Squared = 0.000), meaning that nursing workload does not vary significantly between shifts depending on the hospital. Overall, the model is statistically significant (F = 399.867; p < 0.001) and explains 36.9% of the variance of the NAS scores (R² = 0.369; Adjusted R² = 0.368), which is considered a very good result for ICU data. For further analysis, a Tukey HSD post-HOC test was performed to identify between which hospitals these differences were statistically significant. The results confirmed that all combinations of pairwise comparisons (A-B, A-C and B-C) showed significant differences (all p < 0.001). Hospital B had the highest mean NAS score (M = 106.24, SD = 18.89), significantly exceeding the mean scores of both Hospital A (M = 58.39, SD = 19.78) and C (M = 71.12, SD = 22.73). Hospital C showed a statistically significantly higher NAS level than Hospital A, but lower than Hospital B. The results are presented in Table No. 2. Table 2 Tukey HSD post-HOC results. Comparison Difference (M1-M2) Std. Error p _value 95% CI A-B -47.95 1,08 < 0,001 [-50,49; -45,41] A-C -12.84 0,97 < 0,001 [-15,10; -10,57] B-C 35.11 1,34 < 0,001 [31,98; 38,25] Thus, it can be concluded that nursing workload in ICUs varies significantly depending on the hospital. The highest NAS level at Hospital B could be due to a specific patient profile or unit organisation. These results confirm the need to adapt resources and staff capacity in the context of each hospital. To assess whether NAS score levels differ between day and night shifts regardless of hospital, an independent sample groups T-test was performed. The results indicate that there is no statistically significant difference between the mean values of the NAS scores for the day (M = 65.96; SD = 24.64) and night (M = 65.86; SD = 25.84) shifts (t(3418) = 0.107; p = 0.915), and the effect size (Cohen’s d = 0.004) also indicates little practical significance. No statistically significant differences were found for NAS scores per nurse either (t(3418) = 0.107; p = 0.915). In contrast, statistically significant differences were found in the staff shortage indicator, where there was a smaller shortage of nurses on day shifts (M = 4.53; SD = 5.42) than on night shifts (M = 4.97; SD = 5.33), t(3418) = -2.315; p = 0.021, although the effect size was weak (Cohen’s d = − 0.081). This may be an indication of relatively less available staff at night, which should be taken into account when planning work. The total amount of NAS points per shift was also compared, and statistically significant differences were observed in this aspect as well (t(3418) = -2.883; p = 0.004), but again the effect size was weak (Cohen’s d = -0.101), with a higher total amount of NAS points in day shifts. These results indicate (Table No. 3) that although the average NAS level per nurse does not change significantly between shifts, a higher total number of patients and overall workload is more often observed during day shifts. Table 3 T-test results. Variable Levene’s Test (p) t (df) Sig. (2-tailed) Mean Difference 95% CI of the Difference Cohen’s d NAS total 0.080 0.108 0.915 0.095 –1.65; 1.84 0.004 Nurse shortage 0.002 2.306 0.021 –0.436 –0.81; -0.07 –0.081 NAS total per 1 nurse 0.080 0.108 0.915 0.00095 –0.016; 0.018 0.004 Nas total per 12-hour shift < 0.001 –2.859 0.004 –67.92 –114.5; -21.3 –0.101 To investigate the relationship between patient care intensity (NAS score) and staff resources in the ICU, Pearson correlation analysis was performed. The results reveal several statistically significant correlations, allowing the identification of structural trends and possible gaps in staff availability in relation to care workload. The results are shown in Table No. 4. Table 4 Pearson correlation analysis between NAS scores and staff resources*. NAS sum per 1 protocol NAS sum per 12-hour shift Required number of nurses Actual number of nurses Nurse shortage NAS sum per 1 protocol Pearson Correlation 1 -,276 -,276 -,491 -,142 Sig. (2-tailed) <,001 <,001 <,001 <,001 N 3420 3420 3420 3420 3420 NAS sum per 12-hour shift Pearson Correlation -,276 1 1,000 ,731 ,936 Sig. (2-tailed) <,001 <,001 <,001 <,001 N 3420 3420 3420 3420 3420 Required number of nurses Pearson Correlation -,276 1,000 1 ,731 ,936 Sig. (2-tailed) <,001 <,001 <,001 <,001 N 3420 3420 3420 3420 3420 Actual number of nurses Pearson Correlation -,491 ,731 ,731 1 ,501 Sig. (2-tailed) <,001 <,001 <,001 <,001 N 3420 3420 3420 3420 3420 Nurse shortage Pearson Correlation -,142 ,936 ,936 ,501 1 Sig. (2-tailed) <,001 <,001 <,001 <,001 N 3420 3420 3420 3420 3420 *Note: “Required number of nurses” was directly calculated from “NAS sum per 12-hour shift”; therefore, correlation is mathematically identical (r = 1.000) The analysis revealed a statistically significant weak negative correlation between the NAS sum per protocol and the total NAS score per shift (r = -0.276; p < 0.001), indicating a structural difference, i.e. at higher intensities per episode, the total NAS per shift tends to be slightly lower, possibly due to the distribution of patients. An identical negative correlation was also found between the NAS sum per 1 protocol and the predicted number of nurses per 12 hours, which is also r = -0.276 (p < 0.001), indicating that higher workload episodes may correlate with a lower total amount of care required per twenty-four hours. It reflects the mutual influence of structurally calculated indicators. A significant moderate negative correlation was observed between the NAS sum per 1 protocol and the current number of nurses (r = -0.491; p < 0.001), which is a significant signal of potential resource insufficiency, i.e. higher intensity of patient care is often associated with fewer available staff. These results show that patient care workload and staff resources in ICUs are not always proportionally balanced, and high workload is often not fully compensated by adequate staffing. This highlights the need for more careful planning of human re-source allocation based on actual NAS intensity. To further assess the distribution of NAS scores within each medical treatment institution, a histogram was created with grouping by hospital (Figure No.2). The graph shows the frequency distribution of NAS scores across the three ICUs, allowing a visual assessment of data dispersion and possible structural differences between the institutions. The histogram illustrates significant differences between the hospitals. Hospital B consistently shows the highest workload. Hospital A – the lowest and most even. Hos-pital C – moderate, but with higher variability. This graphical analysis complements the ANOVA and Tukey HSD results, allowing a better understanding of the nature of the distribution of care workload in each institution. The visualisation also points to a possible systemic organisational impact on workload in different hospitals and has an important role to play in future human resources planning decisions. In order to comprehensively assess staff shortages in ICUs, this study analysed the variable “Nurse shortage”, calculated as the difference between actual and required staff resources per shift based on NAS scores. The boxplot diagram (Fig. 3 ) shows the distribution of nurse shortage in each hospital. Hospital A presents the highest median shortage (approximately 9 nurses) and a wide interquartile range, with several extreme outliers exceeding 15 nurses per shift. In Hospital B, the median shortage is lower (around 2–3 nurses) with moderate dispersion, while in Hospital C the median is close to zero, indicating a better balance of human resources. This figure highlights structural differences between hospitals and demonstrates systematic understaffing at Hospital A. Figure No. 4 analyses the nurse shortage simultaneously by hospital and shift (day [D] and night [N]). The data indicates that the shortage in Hospital A remains consistently high in both day and night shifts, with a median close to 9 nurses in both cases, and with a wide dispersion. Hospital B shows a slight increase in night shifts, while at Hospital C the shortage remains low regardless of shift type. This visualisation confirms that time of shift is not the determining factor in the level of shortage, but that the institutional context, hospital resource planning, and work organisation play a more important role. These three graphs together form a comprehensive visual overview of the manifestations of nurse shortage in Latvian ICUs, showing both time dynamics and comparative analysis between hospitals, as well as differences in staffing in different shifts. The visualisations particularly highlight significant staff resource gaps in Hospital A, which may affect the quality of care and the risk of employee burnout, and also reveal a relatively better structured resource allocation in Hospital C. The scatter plot (Fig. 5 ) shows the relationship between the required number of nurses and the NAS workload per nurse, broken down by the three ICUs. The figure demonstrates a clear negative trend: as the required staffing level increases, the workload per nurse decreases. Conversely, lower required staffing is associated with higher individual workload. This visualisation complements the correlation analysis and illustrates structural differences in staffing patterns between hospitals. In addition to the quantitative analysis, extreme cases were identified where the workload per nurse per shift exceeded the theoretical optimal threshold of 100 NAS points. According to the NAS methodology [ 2 ], this value is considered to be the maximum optimal workload that a nurse can safely and qualitatively provide in a 12-hour period. Therefore, exceeding the NAS workload above 130 points is already considered clinically dangerous for both the patient and the healthcare worker. The study found a total of 37 such extreme cases, in which the NAS score per nurse reached or exceeded 130. In Hospital A, 28 extreme cases were recorded, six of which had NAS values of more than 150 points per nurse, with a maximum recorded value of 156.90. In Hospital B, 6 cases were identified with values ranging from 132 to 147 NAS points. Only 3 cases were observed in Hospital C, and in none of them the NAS workload exceeded the 140-point threshold. Most of these extreme cases coincided with shifts with a pronounced staff shortage, with nurse shortage ranging from 6 to 15 nurses per shift, according to NAS estimates. At the same time, a very high total NAS score per shift was observed, especially in Hospital A, where the total NAS sum exceeded 200 points on several occasions, well above the average workload, which ranged around 65–70 points in all hospitals. 4. Discussion This study provides the first systematic assessment of ICU nursing workload in Latvia using NAS. The results demonstrate that nurses in level 3 hospitals experience a substantially higher workload compared to level 2 hospitals. In Hospital A (level 3), the average NAS score exceeded 70%, indicating that one patient requires more than 70% of a nurse’s working time during a shift. By contrast, in hospitals B and C (level 2), average scores were 55–60%, meaning that one patient requires approximately half of a nurse’s time. These differences reflect the severity of patients’ conditions and treatment profiles, with level 3 hospitals treating the most critical cases requiring more intensive care. The findings highlight that Latvian ICU nurses, particularly in tertiary centers, frequently work under conditions of heavy workload, raising concerns about patient safety and staff well-being. High NAS values are not just statistical anomalies but systemic warning signals. When the workload per nurse exceeds safe thresholds, the ability to provide timely and high-quality care is compromised, increasing risks of burnout, erroneous clinical decisions, and adverse events [ 29 – 30 ]. Previous studies show that workload above 130 NAS points may be associated with higher mortality, decreased quality of care, and moral distress among nurses [ 31 – 33 ]. Extreme cases in our dataset therefore underscore the importance of regular workload monitoring and timely interventions to prevent unsafe conditions. Developing action protocols for high workload situations, ensuring replacement staff, and enabling rapid recruitment of additional personnel are essential steps to safeguard both staff and patients. The international context confirms that the Latvian results are consistent with global evidence. A meta-analysis of 70 studies reported an average global NAS of 66.2% [ 31 ]. In France, a study of 105 ICUs reported a median NAS of 61% (IQR 49–80%) [ 32 ], and in Belgium, average scores typically ranged from 65–70%, reaching 70–75% in specialised units such as burn or cardiac surgery [ 34 ]. These figures correspond to our findings, where the 70% workload in Hospital A places Latvia’s tertiary ICU among the highest European ranges, while 55–60% in level 2 hospitals is somewhat below the global mean. Importantly, NAS methodology allows workloads above 100%, reflecting situations where one nurse cares for multiple high-dependency patients simultaneously [ 32 ]. For example, 71% of French ICU nurses reported shift workloads exceeding 100%, clearly signalling staff shortages and patient safety risks [ 32 ]. Our results therefore not only confirm the high workload in Latvian ICUs but also place it in the broader European and international perspective. The COVID-19 pandemic further highlighted the critical role of workload monitoring. In Belgium, COVID-19 patients required an average NAS of 92% compared to 72% for non-COVID patients [ 35 ]. More than half of COVID-19 patients had NAS scores above 76%, and 30% exceeded 100%, demonstrating extreme overload [ 35 ]. These values surpassed statutory norms of one nurse per three patients and confirmed that such ratios underestimate the real requirements of intensive care. Earlier research in Belgium similarly concluded that the 1:3 ratio is inadequate, with NAS calculations suggesting an optimal ratio closer to 1:1.5 [ 36 ]. Comparable data from Brazil also showed that an average NAS of 70% requires a ratio of one nurse per 1–2 patients, as 1:2 is often insufficient [ 37 ]. Against this background, the Latvian findings of > 70% workload in level 3 hospitals point to a level already recognised internationally as critical, calling for urgent reassessment of nurse–patient ratios. The study also sheds light on implementation challenges specific to Latvia. Currently, there is no unified digital infrastructure for workload monitoring. In our pilot project, data were collected digitally every 12 hours [ 26 ], but this is not standard practice across the country. In many hospitals, NAS is not recorded at all, and manual entry can be time-consuming for already overburdened staff. NAS covers more than 20 care activities [ 2 ], and accurate completion requires both training and time. International attempts to facilitate data entry with mobile applications or smart devices have not consistently demonstrated greater accuracy or reliability [ 38 – 39 ]. Therefore, Latvia will need to invest in user-friendly digital solutions, ideally integrated into existing electronic health records, to enable sustainable data collection and minimise the risk of error. Another challenge is accounting for patient flow. Workload can vary considerably depending on admission and discharge dynamics. Studies have shown that workload is highest on the day of admission, when additional tasks such as stabilisation, documentation, and communication with families are required, and lowest on discharge days [ 40 – 41 ]. In our context, such short-term peaks may be underestimated without a system that records patient turnover events. This is especially relevant for hospitals with high patient throughput, where cumulative workload may be obscured if admissions and discharges are not separately analysed. Internationally, some studies have implemented patient-level NAS recording for every shift [ 32 ], which requires high discipline and additional staff training. For Latvia, integrating these practices will be essential to fully capture workload dynamics. The problem of nurse shortage further complicates implementation. Latvia has approximately 4.2 nurses per 1000 population compared to the EU average of 8.5 [ 12 ]. This shortage forces nurses to work overtime and intensifies daily workload. The literature confirms that excessive workload is strongly linked to burnout, job dissatisfaction, and turnover [ 26 , 29 – 30 , 42 – 44 ]. A vicious circle arises: fewer nurses mean higher workload, which in turn accelerates attrition and creates further shortages. In our study, staff shortages were directly visible in several shifts where the required number of nurses exceeded those available. This confirms that workload monitoring cannot be separated from broader workforce policy; addressing shortages is essential both to reduce overload and to ensure reliable data collection. From a health policy perspective, NAS offers an important opportunity to transition to data-driven workforce planning. Traditionally, staffing standards were based on fixed ratios or economic feasibility, but these may not reflect real patient needs [ 45 ]. NAS enables dynamic balancing, signalling when additional staff are required and providing objective justification for resource allocation. International experience demonstrates that systematic NAS monitoring has already informed regulatory discussions, such as in France and Belgium, where findings supported calls to revise statutory nurse–patient ratios [ 32 , 36 ]. The association between workload and patient safety is particularly important: high NAS scores have been linked to increased medication errors, adverse events, and readmissions [ 41 , 46 – 48 ]. This supports the argument that NAS can serve as an early warning tool for healthcare managers and policy makers. When units consistently exceed thresholds of 70–80%, interventions such as reallocating staff, recruiting additional personnel, or adjusting admission policies should be considered. Regular monitoring would also help prevent professional burnout by identifying prolonged overload and allowing for preventive measures such as rotations, supervision, or additional support staff [ 49 ]. For Latvia, implementation of a unified NAS system would require several key steps. First, the use of NAS should be mandated at the policy level, ensuring that all ICUs collect data consistently (e.g., every 12 hours per patient). Our adaptation study confirmed that the Latvian version of NAS has high content validity (CVI = 0.909) [ 26 ], meaning that methodological readiness exists. Second, a centralised digital platform is needed, ideally as a NAS module within the national e-health system, to facilitate easy entry and secure storage. Examples from Brazil, where pilot NAS Cloud systems were developed [ 38 ], illustrate potential models. Third, regular data monitoring should be introduced both within ICUs and at national level. Hospitals could conduct internal audits of NAS statistics on a monthly or quarterly basis, while centralised data would inform national resource planning. Finally, the use of NAS allows flexibility in staffing beyond fixed standards. If a unit receives multiple high-acuity patients with NAS > 80%, management could temporarily allocate additional staff based on objective evidence rather than subjective judgement. In the longer term, consistent NAS monitoring may support revision of current nurse–patient ratios upwards, in line with international practice [ 37 ]. Strengths and limitations. This study provides novel quantitative data on ICU nursing workload in Latvia, aligned with international evidence and based on validated methodology. The strengths include the use of a large dataset and comparison between level 2 and level 3 hospitals. Limitations include reliance on manual data collection, absence of patient-level outcome data, and limited generalisability beyond the three hospitals studied. Nevertheless, the findings provide a strong basis for future research and for the development of a national workload monitoring system. While the NAS provides valuable quantitative insight into ICU nursing workload, it represents only one dimension of a complex phenomenon. Workload is also shaped by patient flow, skill mix, equipment levels, and organisational culture, factors not assessed in this study. Complementary approaches, such as qualitative interviews or outcome-based analyses, would enrich future research and allow a more holistic understanding of nursing workload. In summary, our study demonstrates that nursing workload in Latvian ICUs is high, particularly in tertiary centres where NAS exceeds 70%. The results align with international data showing that ICU nurses often work at or beyond safe workload thresholds. For Latvia, the findings highlight both the urgent need to address staffing shortages and the potential of NAS to provide objective, comparable, and actionable data for workforce planning. Implementing a unified NAS system with digital infrastructure and regular monitoring would not only improve work organisation but also enhance patient safety and sustainability of the nursing workforce. 5. Conclusion This study demonstrated that the NAS can be successfully applied in Latvian ICUs and revealed significant differences in nursing workload between level 2 and level 3 hospitals. While tertiary care showed the highest workload, regional and local ICUs also experienced substantial demands, underscoring the need for context-specific staffing strategies. The findings confirm the practical value of NAS as a standardized, quantitative, and internationally comparable tool for assessing workload and guiding nurse resource planning. Its wider integration into clinical practice and national health policy could strengthen workforce management, reduce risks of burnout, and enhance patient safety. Limitations This study has several limitations that need to be considered when interpreting the results and their applicability in a wider context. First, the scale of the study was limited, data was collected over a three-month period and in only three ICUs, which does not allow for full representativeness in the context of all Latvian ICUs. The study period does not cover a full calendar year, so it is not possible to analyse seasonal variations, such as an increase in the number of patients during the influenza or respiratory infection season. Also, data collection focused on two units of level 2 and one unit of level 3, excluding, for example, paediatric ICUs, which limits the applicability of the results to the full spectrum of intensive care in Latvia. An important limitation concerns the nature of the instrument used; the NAS score is based on the nurse’s individual assessment of the activities performed. Although training was provided prior to data collection, subjectivity in the assessments cannot be completely ruled out. The study did not include parallel independent observer registration or interrater reliability testing, which would have helped to strengthen the reliability of the obtained data. In addition, no comparison instrument, such as the TISS-28 score, was used as a benchmark for assessing the relative effectiveness of the NAS results. Design-wise, the study was designed as a pilot study and therefore did not aim to draw comprehensive conclusions or conclusions that can be generalised in the long term. More complex statistical analysis models were not applied, so the differences found between hospitals and types of shifts are based on descriptive and comparative observations. The data were mostly recorded for day and night shifts, without separate analysis of holidays or emergencies, although work intensity can vary significantly under these circumstances. The variety of shifts not covered by the study and the lack of an assessment of the variation between different time periods reduce the possibility to fully understand the dynamics of the NAS on a daily and seasonal basis. The study also did not analyse the relationship between the NAS results and patient care outcomes such as mortality, complications, or length of stay in the intensive care unit. This limits the clinical interpretation of the NAS as a prognostic or treatment efficacy indicator. Although the study collected nurses’ experiences of the practical use of the NAS, no structured qualitative research, such as interviews or questionnaires, was conducted to allow an in-depth assessment of usability, convenience, and barriers faced by staff. A key limitation is that this study relied exclusively on NAS scores, without including patient outcome measures or qualitative data from nurses. This restricts the interpretation of workload to a schematic, quantitative representation and may not reflect the full complexity of ICU nursing practice. Future studies should combine NAS with structured interviews, observational methods, and outcome indicators to capture the multidimensional nature of workload. Abbreviations NAS The Nursing Activities Score ICU Intensive Care Units TISS-28 Therapeutic Intervention Scoring System – 28 items h Hours CVI Content Validity Index IBM SPSS Statistical Package for the Social Sciences PĒK Pētījuma Ētikas komitēja (Research Ethics Committee) D Day N Night IQR Interquartile Range COVID-19 Coronavirus Disease 2019 Declarations Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Riga Stradiņš University (protocol code 2-PĒK-4/416/2023 09.05.2023). Informed consent was obtained from all subjects involved in the study. Consent for publication: Not applicable Availability of data and materials: The datasets produced and examined in this study can be obtained from the corresponding author upon a reasonable request. All data generated or analyzed during this study are provided within the published article. The data utilized in this study is confidential. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Funding: The APC was funded by Riga Stradiņš University, Department of Nursing and Midwifery (Riga, Latvia). 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10:05:27","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186669,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/abd6aadc455e31bbc5bd255a.html"},{"id":91841898,"identity":"a9f66182-0cb1-4990-b6e9-18981123757d","added_by":"auto","created_at":"2025-09-22 09:57:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":840476,"visible":true,"origin":"","legend":"\u003cp\u003eTotal NAS points per shift (12h) over time in three Latvian ICUs (n = 3420).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/b77bad3a9dc7788ed72d3b21.png"},{"id":91841896,"identity":"0c07786d-a4ee-422c-bbdf-88d4004a9350","added_by":"auto","created_at":"2025-09-22 09:57:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":344266,"visible":true,"origin":"","legend":"\u003cp\u003eStacked Histogram of NAS_sum by Hospital.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/06cb51ab4e3c2b3ab059a8ea.png"},{"id":91847157,"identity":"d9bd6720-c593-4ab8-8bf1-5abaa6c97c08","added_by":"auto","created_at":"2025-09-22 10:21:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93912,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot: Nurse shortage by hospital.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/0b68eefe0868aef766b19611.png"},{"id":91843255,"identity":"8a36db5f-4c27-4133-af7d-814574f3cf57","added_by":"auto","created_at":"2025-09-22 10:05:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":204231,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot: Nurse shortage by hospital and type of shift*. *(D-Day, N-Night)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/0af8d09331d10a04323d3df6.png"},{"id":91841903,"identity":"49013993-9e5b-4196-925d-c372976f8650","added_by":"auto","created_at":"2025-09-22 09:57:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1005412,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of NAS per nurse against the required number of nurses per 12-h shift, by hospital.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/714a95748e6ebbee7d98f6e8.png"},{"id":107929473,"identity":"fd6faec5-786a-42a3-96a2-db0725fbd950","added_by":"auto","created_at":"2026-04-27 16:15:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4190459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7516003/v1/3f6e0484-851f-4fdc-96ba-f382d2776b40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eStudy of Nursing Workload Using the Nursing Activities Score in Level 2 and Level 3 ICUs in Latvia: A 3-Month Observation\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntensive care units (ICUs) are highly specialised structural units where critically ill patients are provided with continuous monitoring and support of vital functions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Nursing Activities Score (NAS) instrument is widely used worldwide to objectively quantify intensive care nursing workload. This instrument was developed in 2003 by Miranda et al. as a modified version of the TISS-28 (Therapeutic Intervention Scoring System) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compared to the traditional TISS-28, the NAS has been supplemented with five new aspects of care: patient monitoring and parametric adjustment, hygiene and mobilisation, care of family members and administrative tasks [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For each of these tasks, a time weighting value is calculated and the sum of these values reflects the actual working time of the nurses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In international studies, the NAS has proven to be an accurate workload measurement instrument and is able to cover approximately 81% of nurses\u0026rsquo; working time capacity, while the TISS-28 only covers approximately 43% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The NAS score has been translated into many languages and implemented in at least 12 countries, including Norway [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Spain [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and Brazil [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Inter-national studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] have shown considerable variability, with the mean NAS value in intensive care units in seven countries being around 72.8%, with a range from 44.5% (Spain) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] to 101.8% (Norway) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This demonstrates the adaptability of the NAS instrument to different healthcare system settings and its usefulness for comparing nursing workload across ICUs.\u003c/p\u003e\u003cp\u003eIn Latvia, the levels of intensive care units and the organisation of their activities are laid down in laws and regulations. Cabinet of Ministers Regulation No. 60 of 2009 on medical treatment institutions and their structural units states that patients in ICUs are under the continuous care and supervision of physicians and nursing staff, receive intensive treatment and support of vital functions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The regulation emphasises that the basic unit of each ICU bed is one patient [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In practical healthcare, Latvian ICUs are divided into three levels: level 1 (low intensity), level 2 (medium intensity) and level 3 (highly specialised) units [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This hierarchical structure reflects the profile of the Latvian healthcare system, with the main university hospitals providing the most specialised intensive care, and the regional ones providing the basic level of care.\u003c/p\u003e\u003cp\u003eThe availability of nurses is one of the most pressing challenges in the context of intensive care, and this problem is particularly pronounced in Latvia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. International organisations predict a shortage of nurses in many countries, especially in those with rapidly ageing populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Latvia, the trend is alarming, with only 418 registered nurses per 100,000 population in 2020 (around 419 in 2021) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while the European Union average was around 770\u0026ndash;850 in 2021, and this figure continues to lag significantly behind [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. ICU leadership and hospital administration experience this deficit most acutely. Although in theory the nurse-to-patient ratio is determined by the level of care, and a ratio of 1:3\u0026ndash;4 is recommend for ICUs [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], these recommendations are not legally binding and have not been integrated into the national regulatory framework. The risk of burnout among nurses is increasing, and many Latvian nurses are choosing to work abroad, where there is a demand for ICU nurses, better working conditions, and more competitive salaries [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To counter these trends, the state needs to ensure sustainable remuneration and an improved working environment to motivate existing staff and attract the next generation.\u003c/p\u003e\u003cp\u003eNursing workload is a concept that describes the time and effort that the nursing team has to invest in patient care [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This workload consists of both direct care tasks, such as dispensing medication and monitoring the patient, and indirect activities, such as documentation and communication with the patient\u0026rsquo;s family [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Excessive workload can lead to professional burnout, emotional exhaustion, and lower job satisfaction [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which in turn contributes to staff turnover and further shortages in healthcare [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For patients, this means an increased risk of medical errors, delayed procedures, and more frequent complications [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Insufficient workload, in its turn, can signal inefficient use of resources [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Healthcare managers need objective information about actual resource consumption to make informed organisational decisions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This is particularly important in intensive care, where patient conditions are severe and care is continuous. Experts emphasise that in intensive care, the workload does not depend only on the severity of the patient\u0026rsquo;s illness, but also on the unit\u0026rsquo;s infrastructure, team organisation, technological support, and other systemic aspects [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Accurate work-load measurement in such circumstances helps to ensure efficient staff distribution, increase patient safety, and protect the health of employees in the long term [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn view of the above, the focus of this study is on the implementation of the NAS and the analysis of its practical application in Latvian intensive care units. The aim of the study is to implement the NAS in the ICUs of three hospitals of different levels (designated A, B, and C), analysing a three-month period from 6 February to 7 May 2025. During this period, data on nurses\u0026rsquo; activities was systematically collected using the NAS as an instrument to quantify workload.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Design and Aim of the Study\u003c/h2\u003e\u003cp\u003eThis study was designed as a prospective, observational study to assess the nursing workload in intensive care units in Latvia using the NAS. The main objective of the study was to identify the actual intensity of care and to identify inter-institutional differences, as well as to provide evidence-based conclusions on the level of staff workload.\u003c/p\u003e\u003cp\u003eData was collected from three Latvian medical treatment institutions with ICUs, representing different levels of care and clinical experience. Hospital A \u0026ndash; a university-type institution of level 3 with highly specialised equipment and a multidisciplinary team. Hospital B and Hospital C \u0026ndash; regional medical treatment institutions of level 2 with different infrastructure and human resources. All participating ICUs provide care for adult patients with life-threatening conditions. The most common patient diagnoses across these ICUs were sepsis, postoperative monitoring after major surgery and respiratory failure requiring mechanical ventilation. In addition to registered nurses, ICU teams included nurse assistants who supported basic care and logistical tasks. All admitted patients were included in the analysis, including those with short ICU stays of less than 24 hours.\u003c/p\u003e\u003cp\u003eThe study used a purposive sample, which was formed by contacting several medical treatment institutions in various regions of Latvia. The inclusion criteria for the sample were that the NAS is used in the intensive care units or there is a possibility of adapting it to the healthcare documentation, structured and regularly completed documentation of patient care processes is available, and it is possible to ensure data anonymisation and processing in accordance with ethical requirements.\u003c/p\u003e\u003cp\u003eThe total study sample consisted of n\u0026thinsp;=\u0026thinsp;3420 patient care episodes recorded over a defined time period, including both day and night shifts, thus providing a comprehensive assessment of workload over a 12- and 24-hour period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data Collection Process\u003c/h2\u003e\u003cp\u003eData collection took place between 06.02.2025 and 07.05.2025, in collaboration with local staff (nurses) in each medical treatment institution. The data was collected using the NAS and recorded on a dedicated digital data entry platform that was developed to facilitate uniform and standardised data entry. During each 12-hour shift, several nurses completed the NAS about their patients based on real-time care or retrospectively documented information in patient care protocols.\u003c/p\u003e\u003cp\u003eAdditional structured data was also collected on type of medical treatment institution (university/regional/private), number of intensive care beds, staff ratio (nurse-to-patient ratio), and shift type (day/night) and date.\u003c/p\u003e\u003cp\u003eThe NAS was selected as the only instrument because it is an internationally validated and comparable tool for quantifying ICU nursing workload. However, it does not capture broader contextual factors (e.g., referral system, nurse competences, or technological resources), which must be considered when interpreting the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Instrument Used\u003c/h2\u003e\u003cp\u003eThe NAS is a validated, internationally used instrument that allows for the quantitative assessment of nursing workload in the care of intensive care patients. The score was developed in 2003 by Miranda et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], based on a modification of the TISS-28 and adapted to include non-invasive but important care activities such as support of the patient\u0026rsquo;s family and documentation.\u003c/p\u003e\u003cp\u003eThe NAS consists of 23 individual care elements covering different aspects of clinical care. Each item is evaluated according to a certain value (for example, 1.a \u0026ndash; 4.5 points, 2\u0026ndash;3.5 points, etc.). It consists of items covering care activities, ventilatory, cardiovascular, renal, neurological and metabolic support, as well as specific interventions such as surgical procedures, diagnostic tests, and patient transportation. Each item has a predefined weight, and the total score ranges from 0 to 177 points, corresponding to the percentage of time a nurse spends caring for one patient during a shift. A score of 100 points is equivalent to the workload of one nurse per shift. A score above 100 indicates that a patient requires the equivalent of more than one full-time nurse. In this study, NAS was completed for every patient in each 12-hour shift. The total NAS points per shift were calculated and divided by 100 to estimate the required number of nurses. All admitted patients were included, including those with short ICU stays of less than 24 hours. No demographic data about the nurses (e.g., sex, years of experience) were collected, as the focus of the study was exclusively on patient-related workload episodes.\u003c/p\u003e\u003cp\u003eThe number of points corresponds to the required time investment by the staff \u0026ndash; 100 points correspond to 12 hours of work by one nurse. The score can be used as an assessment of a 12-hour or 24-hour work shift.\u003c/p\u003e\u003cp\u003eIn Latvia, the NAS was adapted a little earlier and validated in the conditions of one unit. Cerela-Boltunova et al, in 2022 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], provided a translation into Latvian, which experts rejected and clarified individual items, subsequently piloting the score in an intensive care unit (42 patients, NAS completed 226 times). They reported very good reliability of the score (Cronbach\u0026rsquo;s alpha 0.973) and CVI\u0026thinsp;=\u0026thinsp;0.909 of the final version, confirming the suitability of the NAS for the Latvian environment. However, until now, there was a lack of research comparing NAS results between different intensive care units in Latvia or analysing the usability of this score in practice.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data Entry and Processing\u003c/h2\u003e\u003cp\u003eAfter the data was collected using the NAS, the results were automatically exported from the dedicated platform in a structured Excel format. The data sets were checked and prepared for further statistical processing using IBM SPSS Statistics 28.0 software. Statistical analyses were performed by the first author, who has advanced training in SPSS, in consultation with a certified statistician to ensure methodological accuracy.\u003c/p\u003e\u003cp\u003eFirst, initial data cleaning was performed, including missing value checking and logical error correction, as well as data adequacy validation for each variable. For each episode of care, the total NAS score in a 12-hour shift was calculated. As the hospitals included in the study represented different levels of care, special attention was paid to the analysis of inter-institutional differences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics included frequency distributions, mean, SD, median, as well as min and max values. Visual representations, including histograms, boxplots, and bar graphs, were created to analyse the distribution and possible asymmetry of the data.\u003c/p\u003e\u003cp\u003eFor further analysis, the normality of data distribution was tested using Kolmogorov-Smirnov and Shapiro-Wilk tests. The most appropriate inferential statistical tests were selected according to the results. One-way analysis of variance (ANOVA) technique was used to compare NAS means between hospitals if the data distribution was close to normal. If the data distribution did not meet the normality assumptions, the non-parametric Kruskal-Wallis H test was used to identify statistically significant differences between hospitals. Mann-Whitney U tests were performed for additional analyses between two hospitals.\u003c/p\u003e\u003cp\u003eThe results of the analysis showed that the mean NAS score in Hospital A (level 3 care) was significantly higher than in Hospitals B and C (level 2), and this difference was statistically significant (Kruskal-Wallis, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn addition, linear regression analysis was conducted to evaluate the relationship between the NAS per nurse and the required number of nurses. Regression coefficients, intercept, significance values, and the coefficient of determination (R\u0026sup2;) were calculated to assess the strength and direction of the relationship. The regression line and equation were obtained from SPSS version 28.0 output, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Ethical Consideration\u003c/h2\u003e\u003cp\u003eThe study was conducted in compliance with the ethical principles [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Latvian laws and regulations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The ethics committees of all three participating hospitals approved the project prior to the data collection. No additional interventions were made in the treatment of patients during the study. Completion of the NAS questionnaire was part of the clinical practice monitoring. All questionnaires were anonymised and no personal or specific health data of patients were collected that could reveal individual information. The nurses\u0026rsquo; participation in the study was voluntary and it was explained to them in advance that the results would be analysed only in collective summaries. They thus had no additional trust concerns regarding their own privacy and that of their patients. Confidentiality was ensured in data processing and storage in accordance with the Ministry of Welfare guidelines on patient and staff protection [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Ethical approval for the study was obtained from the Ethics Committee of Riga Stradiņš University (Decision No. 2-PĒK-4/416/2023, 09 May 2023).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 3420 completed NAS protocols were obtained in the study from three Latvian ICUs representing different levels of care. The data collection took place over a period of three months (6 February to 7 May 2025), covering both day and night 12-hour shifts.\u003c/p\u003e\u003cp\u003eTable No. 1 presents basic information on the number of completed protocols, average NAS scores, SD, medians, as well as min and max values in each hospital and by shift. It provides an overview of the intensity and distribution of nursing workload across the three participating medical treatment institutions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNAS score distribution and descriptive statistics for different hospitals and shifts.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of protocols\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of protocols (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of protocols (night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58,38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106,96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71,31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65,87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20,14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24,61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25,84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of nurses (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58,13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105,18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70,85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65,96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD (night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19,69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25,84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of nurses (night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e95% CI Lower Bound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104,38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69,18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e95% CI Upper Bound\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108,09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73,07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0,281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (total)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55,00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103,30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71,60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Mean (overall)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58,39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106,24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65,91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal SD (overall)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22,73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25,28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Mean (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e65,87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal SD (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e25,84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Mena (night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e65,96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal SD (night)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e25,84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin NAS points per 1 nurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21,40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33,30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25,80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax NAS points per 1 nurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156,90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e154,30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin NAS points per 12-hour shift\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25,80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54,40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax NAS points per 12-hour shift\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2620,80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e846,70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e764,20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure No. 1 illustrates the workload dynamics in different hospitals and allows the identification of peak workload days. Hospital B consistently shows a higher NAS score than Hospitals A and C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess statistically significant differences in nursing workload between the three ICUs (Hospital A, B, and C), an analysis of variance (One-Way ANOVA) was performed using the sum of NAS scores as the dependent variable. The results show that there are statistically significant differences between hospitals in the NAS score levels (F(2, 3417)\u0026thinsp;=\u0026thinsp;954.415, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and Partial Eta Squared\u0026thinsp;=\u0026thinsp;0.359), indicating a significant effect of the hospital factor on nursing workload. Factors such as \u0026ldquo;Shift\u0026rdquo; are not statistically significant (F\u0026thinsp;=\u0026thinsp;0.764; p\u0026thinsp;=\u0026thinsp;0.382) and the effect size is also insignificant (Partial Eta Squared\u0026thinsp;=\u0026thinsp;0.000). So there is no significant difference in the NAS score between day and night shifts. The interaction between hospital and shift is also not significant (F\u0026thinsp;=\u0026thinsp;0.233; p\u0026thinsp;=\u0026thinsp;0.792; Partial Eta Squared\u0026thinsp;=\u0026thinsp;0.000), meaning that nursing workload does not vary significantly between shifts depending on the hospital.\u003c/p\u003e\u003cp\u003eOverall, the model is statistically significant (F\u0026thinsp;=\u0026thinsp;399.867; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and explains 36.9% of the variance of the NAS scores (R\u0026sup2; = 0.369; Adjusted R\u0026sup2; = 0.368), which is considered a very good result for ICU data.\u003c/p\u003e\u003cp\u003eFor further analysis, a Tukey HSD post-HOC test was performed to identify between which hospitals these differences were statistically significant. The results confirmed that all combinations of pairwise comparisons (A-B, A-C and B-C) showed significant differences (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hospital B had the highest mean NAS score (M\u0026thinsp;=\u0026thinsp;106.24, SD\u0026thinsp;=\u0026thinsp;18.89), significantly exceeding the mean scores of both Hospital A (M\u0026thinsp;=\u0026thinsp;58.39, SD\u0026thinsp;=\u0026thinsp;19.78) and C (M\u0026thinsp;=\u0026thinsp;71.12, SD\u0026thinsp;=\u0026thinsp;22.73). Hospital C showed a statistically significantly higher NAS level than Hospital A, but lower than Hospital B. The results are presented in Table No. 2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTukey HSD post-HOC results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDifference (M1-M2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e_value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-47.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-50,49; -45,41]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-12.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-15,10; -10,57]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[31,98; 38,25]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThus, it can be concluded that nursing workload in ICUs varies significantly depending on the hospital. The highest NAS level at Hospital B could be due to a specific patient profile or unit organisation. These results confirm the need to adapt resources and staff capacity in the context of each hospital.\u003c/p\u003e\u003cp\u003eTo assess whether NAS score levels differ between day and night shifts regardless of hospital, an independent sample groups T-test was performed. The results indicate that there is no statistically significant difference between the mean values of the NAS scores for the day (M\u0026thinsp;=\u0026thinsp;65.96; SD\u0026thinsp;=\u0026thinsp;24.64) and night (M\u0026thinsp;=\u0026thinsp;65.86; SD\u0026thinsp;=\u0026thinsp;25.84) shifts (t(3418)\u0026thinsp;=\u0026thinsp;0.107; p\u0026thinsp;=\u0026thinsp;0.915), and the effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.004) also indicates little practical significance. No statistically significant differences were found for NAS scores per nurse either (t(3418)\u0026thinsp;=\u0026thinsp;0.107; p\u0026thinsp;=\u0026thinsp;0.915).\u003c/p\u003e\u003cp\u003eIn contrast, statistically significant differences were found in the staff shortage indicator, where there was a smaller shortage of nurses on day shifts (M\u0026thinsp;=\u0026thinsp;4.53; SD\u0026thinsp;=\u0026thinsp;5.42) than on night shifts (M\u0026thinsp;=\u0026thinsp;4.97; SD\u0026thinsp;=\u0026thinsp;5.33), t(3418) = -2.315; p\u0026thinsp;=\u0026thinsp;0.021, although the effect size was weak (Cohen\u0026rsquo;s d = \u0026minus;\u0026thinsp;0.081). This may be an indication of relatively less available staff at night, which should be taken into account when planning work.\u003c/p\u003e\u003cp\u003eThe total amount of NAS points per shift was also compared, and statistically significant differences were observed in this aspect as well (t(3418) = -2.883; p\u0026thinsp;=\u0026thinsp;0.004), but again the effect size was weak (Cohen\u0026rsquo;s d = -0.101), with a higher total amount of NAS points in day shifts.\u003c/p\u003e\u003cp\u003eThese results indicate (Table No. 3) that although the average NAS level per nurse does not change significantly between shifts, a higher total number of patients and overall workload is more often observed during day shifts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eT-test results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevene\u0026rsquo;s Test (p)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et (df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI of the Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNAS total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;1.65; 1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNurse shortage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.81; -0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.081\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNAS total per 1 nurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.016; 0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNas total per 12-hour shift\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;2.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;67.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;114.5; -21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.101\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo investigate the relationship between patient care intensity (NAS score) and staff resources in the ICU, Pearson correlation analysis was performed. The results reveal several statistically significant correlations, allowing the identification of structural trends and possible gaps in staff availability in relation to care workload. The results are shown in Table No. 4.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlation analysis between NAS scores and staff resources*.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNAS sum per 1 protocol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNAS sum per 12-hour shift\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRequired number of nurses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eActual number of nurses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNurse shortage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNAS sum per 1 protocol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-,276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-,276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-,491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-,142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNAS sum per 12-hour shift\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-,276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e,731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e,936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRequired number of nurses\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-,276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e,731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e,936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eActual number of nurses\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-,491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e,731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e,731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e,501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNurse shortage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-,142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e,936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e,936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e,501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Note: \u0026ldquo;Required number of nurses\u0026rdquo; was directly calculated from \u0026ldquo;NAS sum per 12-hour shift\u0026rdquo;; therefore, correlation is mathematically identical (r\u0026thinsp;=\u0026thinsp;1.000)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe analysis revealed a statistically significant weak negative correlation between the NAS sum per protocol and the total NAS score per shift (r = -0.276; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a structural difference, i.e. at higher intensities per episode, the total NAS per shift tends to be slightly lower, possibly due to the distribution of patients.\u003c/p\u003e\u003cp\u003eAn identical negative correlation was also found between the NAS sum per 1 protocol and the predicted number of nurses per 12 hours, which is also r = -0.276 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher workload episodes may correlate with a lower total amount of care required per twenty-four hours. It reflects the mutual influence of structurally calculated indicators.\u003c/p\u003e\u003cp\u003eA significant moderate negative correlation was observed between the NAS sum per 1 protocol and the current number of nurses (r = -0.491; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is a significant signal of potential resource insufficiency, i.e. higher intensity of patient care is often associated with fewer available staff.\u003c/p\u003e\u003cp\u003eThese results show that patient care workload and staff resources in ICUs are not always proportionally balanced, and high workload is often not fully compensated by adequate staffing. This highlights the need for more careful planning of human re-source allocation based on actual NAS intensity.\u003c/p\u003e\u003cp\u003eTo further assess the distribution of NAS scores within each medical treatment institution, a histogram was created with grouping by hospital (Figure No.2). The graph shows the frequency distribution of NAS scores across the three ICUs, allowing a visual assessment of data dispersion and possible structural differences between the institutions.\u003c/p\u003e\u003cp\u003eThe histogram illustrates significant differences between the hospitals. Hospital B consistently shows the highest workload. Hospital A \u0026ndash; the lowest and most even. Hos-pital C \u0026ndash; moderate, but with higher variability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis graphical analysis complements the ANOVA and Tukey HSD results, allowing a better understanding of the nature of the distribution of care workload in each institution. The visualisation also points to a possible systemic organisational impact on workload in different hospitals and has an important role to play in future human resources planning decisions.\u003c/p\u003e\u003cp\u003eIn order to comprehensively assess staff shortages in ICUs, this study analysed the variable \u0026ldquo;Nurse shortage\u0026rdquo;, calculated as the difference between actual and required staff resources per shift based on NAS scores.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe boxplot diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows the distribution of nurse shortage in each hospital. Hospital A presents the highest median shortage (approximately 9 nurses) and a wide interquartile range, with several extreme outliers exceeding 15 nurses per shift. In Hospital B, the median shortage is lower (around 2\u0026ndash;3 nurses) with moderate dispersion, while in Hospital C the median is close to zero, indicating a better balance of human resources. This figure highlights structural differences between hospitals and demonstrates systematic understaffing at Hospital A.\u003c/p\u003e\u003cp\u003eFigure No. 4 analyses the nurse shortage simultaneously by hospital and shift (day [D] and night [N]). The data indicates that the shortage in Hospital A remains consistently high in both day and night shifts, with a median close to 9 nurses in both cases, and with a wide dispersion. Hospital B shows a slight increase in night shifts, while at Hospital C the shortage remains low regardless of shift type. This visualisation confirms that time of shift is not the determining factor in the level of shortage, but that the institutional context, hospital resource planning, and work organisation play a more important role.\u003c/p\u003e\u003cp\u003eThese three graphs together form a comprehensive visual overview of the manifestations of nurse shortage in Latvian ICUs, showing both time dynamics and comparative analysis between hospitals, as well as differences in staffing in different shifts. The visualisations particularly highlight significant staff resource gaps in Hospital A, which may affect the quality of care and the risk of employee burnout, and also reveal a relatively better structured resource allocation in Hospital C.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe scatter plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) shows the relationship between the required number of nurses and the NAS workload per nurse, broken down by the three ICUs. The figure demonstrates a clear negative trend: as the required staffing level increases, the workload per nurse decreases. Conversely, lower required staffing is associated with higher individual workload. This visualisation complements the correlation analysis and illustrates structural differences in staffing patterns between hospitals.\u003c/p\u003e\u003cp\u003eIn addition to the quantitative analysis, extreme cases were identified where the workload per nurse per shift exceeded the theoretical optimal threshold of 100 NAS points. According to the NAS methodology [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], this value is considered to be the maximum optimal workload that a nurse can safely and qualitatively provide in a 12-hour period. Therefore, exceeding the NAS workload above 130 points is already considered clinically dangerous for both the patient and the healthcare worker. The study found a total of 37 such extreme cases, in which the NAS score per nurse reached or exceeded 130. In Hospital A, 28 extreme cases were recorded, six of which had NAS values of more than 150 points per nurse, with a maximum recorded value of 156.90. In Hospital B, 6 cases were identified with values ranging from 132 to 147 NAS points. Only 3 cases were observed in Hospital C, and in none of them the NAS workload exceeded the 140-point threshold.\u003c/p\u003e\u003cp\u003eMost of these extreme cases coincided with shifts with a pronounced staff shortage, with nurse shortage ranging from 6 to 15 nurses per shift, according to NAS estimates. At the same time, a very high total NAS score per shift was observed, especially in Hospital A, where the total NAS sum exceeded 200 points on several occasions, well above the average workload, which ranged around 65\u0026ndash;70 points in all hospitals.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the first systematic assessment of ICU nursing workload in Latvia using NAS. The results demonstrate that nurses in level 3 hospitals experience a substantially higher workload compared to level 2 hospitals. In Hospital A (level 3), the average NAS score exceeded 70%, indicating that one patient requires more than 70% of a nurse\u0026rsquo;s working time during a shift. By contrast, in hospitals B and C (level 2), average scores were 55\u0026ndash;60%, meaning that one patient requires approximately half of a nurse\u0026rsquo;s time. These differences reflect the severity of patients\u0026rsquo; conditions and treatment profiles, with level 3 hospitals treating the most critical cases requiring more intensive care. The findings highlight that Latvian ICU nurses, particularly in tertiary centers, frequently work under conditions of heavy workload, raising concerns about patient safety and staff well-being.\u003c/p\u003e\u003cp\u003eHigh NAS values are not just statistical anomalies but systemic warning signals. When the workload per nurse exceeds safe thresholds, the ability to provide timely and high-quality care is compromised, increasing risks of burnout, erroneous clinical decisions, and adverse events [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Previous studies show that workload above 130 NAS points may be associated with higher mortality, decreased quality of care, and moral distress among nurses [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Extreme cases in our dataset therefore underscore the importance of regular workload monitoring and timely interventions to prevent unsafe conditions. Developing action protocols for high workload situations, ensuring replacement staff, and enabling rapid recruitment of additional personnel are essential steps to safeguard both staff and patients.\u003c/p\u003e\u003cp\u003eThe international context confirms that the Latvian results are consistent with global evidence. A meta-analysis of 70 studies reported an average global NAS of 66.2% [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In France, a study of 105 ICUs reported a median NAS of 61% (IQR 49\u0026ndash;80%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and in Belgium, average scores typically ranged from 65\u0026ndash;70%, reaching 70\u0026ndash;75% in specialised units such as burn or cardiac surgery [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These figures correspond to our findings, where the 70% workload in Hospital A places Latvia\u0026rsquo;s tertiary ICU among the highest European ranges, while 55\u0026ndash;60% in level 2 hospitals is somewhat below the global mean. Importantly, NAS methodology allows workloads above 100%, reflecting situations where one nurse cares for multiple high-dependency patients simultaneously [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For example, 71% of French ICU nurses reported shift workloads exceeding 100%, clearly signalling staff shortages and patient safety risks [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our results therefore not only confirm the high workload in Latvian ICUs but also place it in the broader European and international perspective.\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic further highlighted the critical role of workload monitoring. In Belgium, COVID-19 patients required an average NAS of 92% compared to 72% for non-COVID patients [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. More than half of COVID-19 patients had NAS scores above 76%, and 30% exceeded 100%, demonstrating extreme overload [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These values surpassed statutory norms of one nurse per three patients and confirmed that such ratios underestimate the real requirements of intensive care. Earlier research in Belgium similarly concluded that the 1:3 ratio is inadequate, with NAS calculations suggesting an optimal ratio closer to 1:1.5 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Comparable data from Brazil also showed that an average NAS of 70% requires a ratio of one nurse per 1\u0026ndash;2 patients, as 1:2 is often insufficient [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Against this background, the Latvian findings of \u0026gt;\u0026thinsp;70% workload in level 3 hospitals point to a level already recognised internationally as critical, calling for urgent reassessment of nurse\u0026ndash;patient ratios.\u003c/p\u003e\u003cp\u003eThe study also sheds light on implementation challenges specific to Latvia. Currently, there is no unified digital infrastructure for workload monitoring. In our pilot project, data were collected digitally every 12 hours [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], but this is not standard practice across the country. In many hospitals, NAS is not recorded at all, and manual entry can be time-consuming for already overburdened staff. NAS covers more than 20 care activities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and accurate completion requires both training and time. International attempts to facilitate data entry with mobile applications or smart devices have not consistently demonstrated greater accuracy or reliability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, Latvia will need to invest in user-friendly digital solutions, ideally integrated into existing electronic health records, to enable sustainable data collection and minimise the risk of error.\u003c/p\u003e\u003cp\u003eAnother challenge is accounting for patient flow. Workload can vary considerably depending on admission and discharge dynamics. Studies have shown that workload is highest on the day of admission, when additional tasks such as stabilisation, documentation, and communication with families are required, and lowest on discharge days [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In our context, such short-term peaks may be underestimated without a system that records patient turnover events. This is especially relevant for hospitals with high patient throughput, where cumulative workload may be obscured if admissions and discharges are not separately analysed. Internationally, some studies have implemented patient-level NAS recording for every shift [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which requires high discipline and additional staff training. For Latvia, integrating these practices will be essential to fully capture workload dynamics.\u003c/p\u003e\u003cp\u003eThe problem of nurse shortage further complicates implementation. Latvia has approximately 4.2 nurses per 1000 population compared to the EU average of 8.5 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This shortage forces nurses to work overtime and intensifies daily workload. The literature confirms that excessive workload is strongly linked to burnout, job dissatisfaction, and turnover [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A vicious circle arises: fewer nurses mean higher workload, which in turn accelerates attrition and creates further shortages. In our study, staff shortages were directly visible in several shifts where the required number of nurses exceeded those available. This confirms that workload monitoring cannot be separated from broader workforce policy; addressing shortages is essential both to reduce overload and to ensure reliable data collection.\u003c/p\u003e\u003cp\u003eFrom a health policy perspective, NAS offers an important opportunity to transition to data-driven workforce planning. Traditionally, staffing standards were based on fixed ratios or economic feasibility, but these may not reflect real patient needs [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. NAS enables dynamic balancing, signalling when additional staff are required and providing objective justification for resource allocation. International experience demonstrates that systematic NAS monitoring has already informed regulatory discussions, such as in France and Belgium, where findings supported calls to revise statutory nurse\u0026ndash;patient ratios [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The association between workload and patient safety is particularly important: high NAS scores have been linked to increased medication errors, adverse events, and readmissions [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This supports the argument that NAS can serve as an early warning tool for healthcare managers and policy makers. When units consistently exceed thresholds of 70\u0026ndash;80%, interventions such as reallocating staff, recruiting additional personnel, or adjusting admission policies should be considered. Regular monitoring would also help prevent professional burnout by identifying prolonged overload and allowing for preventive measures such as rotations, supervision, or additional support staff [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor Latvia, implementation of a unified NAS system would require several key steps. First, the use of NAS should be mandated at the policy level, ensuring that all ICUs collect data consistently (e.g., every 12 hours per patient). Our adaptation study confirmed that the Latvian version of NAS has high content validity (CVI\u0026thinsp;=\u0026thinsp;0.909) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], meaning that methodological readiness exists. Second, a centralised digital platform is needed, ideally as a NAS module within the national e-health system, to facilitate easy entry and secure storage. Examples from Brazil, where pilot NAS Cloud systems were developed [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], illustrate potential models. Third, regular data monitoring should be introduced both within ICUs and at national level. Hospitals could conduct internal audits of NAS statistics on a monthly or quarterly basis, while centralised data would inform national resource planning. Finally, the use of NAS allows flexibility in staffing beyond fixed standards. If a unit receives multiple high-acuity patients with NAS\u0026thinsp;\u0026gt;\u0026thinsp;80%, management could temporarily allocate additional staff based on objective evidence rather than subjective judgement. In the longer term, consistent NAS monitoring may support revision of current nurse\u0026ndash;patient ratios upwards, in line with international practice [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStrengths and limitations. This study provides novel quantitative data on ICU nursing workload in Latvia, aligned with international evidence and based on validated methodology. The strengths include the use of a large dataset and comparison between level 2 and level 3 hospitals. Limitations include reliance on manual data collection, absence of patient-level outcome data, and limited generalisability beyond the three hospitals studied. Nevertheless, the findings provide a strong basis for future research and for the development of a national workload monitoring system.\u003c/p\u003e\u003cp\u003eWhile the NAS provides valuable quantitative insight into ICU nursing workload, it represents only one dimension of a complex phenomenon. Workload is also shaped by patient flow, skill mix, equipment levels, and organisational culture, factors not assessed in this study. Complementary approaches, such as qualitative interviews or outcome-based analyses, would enrich future research and allow a more holistic understanding of nursing workload.\u003c/p\u003e\u003cp\u003eIn summary, our study demonstrates that nursing workload in Latvian ICUs is high, particularly in tertiary centres where NAS exceeds 70%. The results align with international data showing that ICU nurses often work at or beyond safe workload thresholds. For Latvia, the findings highlight both the urgent need to address staffing shortages and the potential of NAS to provide objective, comparable, and actionable data for workforce planning. Implementing a unified NAS system with digital infrastructure and regular monitoring would not only improve work organisation but also enhance patient safety and sustainability of the nursing workforce.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrated that the NAS can be successfully applied in Latvian ICUs and revealed significant differences in nursing workload between level 2 and level 3 hospitals. While tertiary care showed the highest workload, regional and local ICUs also experienced substantial demands, underscoring the need for context-specific staffing strategies. The findings confirm the practical value of NAS as a standardized, quantitative, and internationally comparable tool for assessing workload and guiding nurse resource planning. Its wider integration into clinical practice and national health policy could strengthen workforce management, reduce risks of burnout, and enhance patient safety.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations that need to be considered when interpreting the results and their applicability in a wider context. First, the scale of the study was limited, data was collected over a three-month period and in only three ICUs, which does not allow for full representativeness in the context of all Latvian ICUs. The study period does not cover a full calendar year, so it is not possible to analyse seasonal variations, such as an increase in the number of patients during the influenza or respiratory infection season. Also, data collection focused on two units of level 2 and one unit of level 3, excluding, for example, paediatric ICUs, which limits the applicability of the results to the full spectrum of intensive care in Latvia.\u003c/p\u003e\u003cp\u003eAn important limitation concerns the nature of the instrument used; the NAS score is based on the nurse\u0026rsquo;s individual assessment of the activities performed. Although training was provided prior to data collection, subjectivity in the assessments cannot be completely ruled out. The study did not include parallel independent observer registration or interrater reliability testing, which would have helped to strengthen the reliability of the obtained data. In addition, no comparison instrument, such as the TISS-28 score, was used as a benchmark for assessing the relative effectiveness of the NAS results.\u003c/p\u003e\u003cp\u003eDesign-wise, the study was designed as a pilot study and therefore did not aim to draw comprehensive conclusions or conclusions that can be generalised in the long term. More complex statistical analysis models were not applied, so the differences found between hospitals and types of shifts are based on descriptive and comparative observations. The data were mostly recorded for day and night shifts, without separate analysis of holidays or emergencies, although work intensity can vary significantly under these circumstances. The variety of shifts not covered by the study and the lack of an assessment of the variation between different time periods reduce the possibility to fully understand the dynamics of the NAS on a daily and seasonal basis.\u003c/p\u003e\u003cp\u003eThe study also did not analyse the relationship between the NAS results and patient care outcomes such as mortality, complications, or length of stay in the intensive care unit. This limits the clinical interpretation of the NAS as a prognostic or treatment efficacy indicator. Although the study collected nurses\u0026rsquo; experiences of the practical use of the NAS, no structured qualitative research, such as interviews or questionnaires, was conducted to allow an in-depth assessment of usability, convenience, and barriers faced by staff.\u003c/p\u003e\u003cp\u003eA key limitation is that this study relied exclusively on NAS scores, without including patient outcome measures or qualitative data from nurses. This restricts the interpretation of workload to a schematic, quantitative representation and may not reflect the full complexity of ICU nursing practice. Future studies should combine NAS with structured interviews, observational methods, and outcome indicators to capture the multidimensional nature of workload.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNAS The Nursing Activities Score\u003c/p\u003e\n\u003cp\u003eICU Intensive Care Units\u003c/p\u003e\n\u003cp\u003eTISS-28 Therapeutic Intervention Scoring System \u0026ndash; 28 items\u003c/p\u003e\n\u003cp\u003eh Hours\u003c/p\u003e\n\u003cp\u003eCVI Content Validity Index\u003c/p\u003e\n\u003cp\u003eIBM SPSS Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003ePĒK Pētījuma Ētikas komitēja (Research Ethics Committee)\u003c/p\u003e\n\u003cp\u003eD Day\u003c/p\u003e\n\u003cp\u003eN Night\u003c/p\u003e\n\u003cp\u003eIQR Interquartile Range\u003c/p\u003e\n\u003cp\u003eCOVID-19 Coronavirus Disease 2019\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Riga Stradiņ\u0026scaron; University (protocol code 2-PĒK-4/416/2023 09.05.2023). Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The datasets produced and examined in this study can be obtained from the corresponding author upon a reasonable request. All data generated or analyzed during this study are provided within the published article. The data utilized in this study is confidential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The APC was funded by Riga Stradiņ\u0026scaron; University, Department of Nursing and Midwifery (Riga, Latvia).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, O.C.-B. and I.M.; methodology, O.C.-B.; software, O.C.-B.; validation, O.C.-B., I.M.; formal analysis, O.C.-B.; investigation, O.C.-B. and I.M.; resources, O.C.-B.; data curation, O.C.-B.; writing\u0026mdash;original draft preparation, O.C.-B.; writing\u0026mdash;review and editing, O.C.-B. and I.M.; visualization, O.C.-B.; supervision, I.M.; project administration, O.C.-B.; funding acquisition, I.M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\u003cp\u003eClinical Trial Registration: Not applicable\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChristensen, M.; Liang, M. Critical Care: A Concept Analysis. Int. J. Nurs. Sci. 2023, 10(3), 403\u0026ndash;413. https://doi.org/10.1016/j.ijnss.2023.06.020 \u003c/li\u003e\n\u003cli\u003eMiranda, D.R.; Nap, R.; de Rijk, A.; Schaufeli, W.; Iapichino, G. Nursing Activities Score. Crit. Care Med. 2003, 31(2), 374\u0026ndash;382. https://doi.org/10.1097/01.CCM.0000045567.78801.CC \u003c/li\u003e\n\u003cli\u003ePadilha, K.G.; de Sousa, R.M.C.; Kimura, M.; Miyadahira, A.M.; da Cruz, D.A.L.M.; Vattimo, M.F.F.; Marmol, M.T.; Gallotti, R.M.D.; Mendes, C.C. Nursing Activities Score: An Updated Guideline for Its Application in the Intensive Care Unit. Rev. Esc. 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Cureus 2023, 15, e51159. https://doi.org/10.7759/cureus.51159 \u003c/li\u003e\n\u003cli\u003eBowblis, J.R.; Applebaum, R.; Intrator, O.; et al. Nursing Homes Increasingly Rely on Staffing Agencies for Direct Care Nursing: Study Examines Nursing Home Staffing. Health Aff. 2024, 43(3), 327\u0026ndash;335. https://doi.org/10.1377/hlthaff.2023.01101 \u003c/li\u003e\n\u003cli\u003eDall\u0026rsquo;Ora, C.; Griffiths, P.; Ball, J.; et al. Shift Work Characteristics and Burnout among Nurses: Cross-Sectional Survey. Occup. Med. 2023, 73(4), 199\u0026ndash;204. https://doi.org/10.1093/occmed/kqad046 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"workload, intensive care units, Latvia, health workforce","lastPublishedDoi":"10.21203/rs.3.rs-7516003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7516003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a three-month prospective observational analysis of nursing workload in Intensive Care Units (ICUs) in Latvia using the Nursing Activities Score (NAS). The objective was to implement the NAS instrument in three hospitals representing different levels of care (level II and III) and to assess inter-institutional differences in workload. Data were collected from 3420 nursing care episodes across day and night shifts between February and May 2025. Descriptive and inferential statistics revealed statistically significant differences in NAS scores between the hospitals, with the highest workload observed in the level III facility. No significant difference was found between day and night shifts in overall NAS per nurse, but a significantly higher nurse shortage was identified during night shifts. Strong correlations were observed between NAS total scores, required nursing staff, and actual staffing levels, highlighting critical disparities. Several extreme overload cases were identified, exceeding the safe workload threshold of 100 NAS points per nurse per shift. The findings underscore the need for structured NAS-based workforce planning in Latvian ICUs, particularly in higher-level hospitals. The study demonstrates the NAS instrument's practical applicability for workload monitoring and suggests that its broader implementation can enhance patient safety and staff well-being.\u003c/p\u003e","manuscriptTitle":"Study of Nursing Workload Using the Nursing Activities Score in Level 2 and Level 3 ICUs in Latvia: A 3-Month Observation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:57:21","doi":"10.21203/rs.3.rs-7516003/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T10:35:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T21:24:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T12:17:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81806395915109666407370148199347384958","date":"2026-01-15T14:22:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314030041603670866902022382330919173869","date":"2026-01-13T07:21:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252303222456512019069295991433637642857","date":"2025-11-24T17:09:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T14:15:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208623889058386256238676793861323812307","date":"2025-10-21T08:06:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318819399718990320567084047334352676251","date":"2025-10-06T15:36:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327037286219189469669451904456326460132","date":"2025-10-01T13:13:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219569395010173306038582760685870832183","date":"2025-09-13T17:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T16:34:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-06T06:50:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-06T06:49:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-09-02T09:22:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"611e22e6-c758-4d82-a6a7-5720565c218f","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:15:38+00:00","versionOfRecord":{"articleIdentity":"rs-7516003","link":"https://doi.org/10.1186/s12912-026-04651-7","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2026-04-20 15:59:56","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-09-22 09:57:21","video":"","vorDoi":"10.1186/s12912-026-04651-7","vorDoiUrl":"https://doi.org/10.1186/s12912-026-04651-7","workflowStages":[]},"version":"v1","identity":"rs-7516003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7516003","identity":"rs-7516003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}