Perceived workload of healthcare providers delivering free maternal services: An analytical cross-sectional etudy based on the National Aeronautics and Space Administration Task Load Index (NASA-TLX) in primary health care facilities in Kananga, Democratic Republic of the Congo

Perceived workload of healthcare providers delivering free maternal services: An analytical cross-sectional etudy based on the National Aeronautics and Space Administration Task Load Index (NASA-TLX) in primary health care facilities in Kananga, Democratic Republic of the Congo | 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 Perceived workload of healthcare providers delivering free maternal services: An analytical cross-sectional etudy based on the National Aeronautics and Space Administration Task Load Index (NASA-TLX) in primary health care facilities in Kananga, Democratic Republic of the Congo Paulin Nkolamoyo Musungula, David Pamutena Mashingu, Jean Claude Djulu Shamanga, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9075820/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Since 2021, the Democratic Republic of the Congo has implemented a free maternity care policy. This policy is likely to increase the demand for maternal health services, potentially creating additional challenges in managing workload if it is not accompanied by an adequate health workforce. This study aimed to quantify the perceived workload of healthcare providers and identify associated factors within this specific policy context. Methods An analytical cross-sectional study was conducted among 129 healthcare professionals (midwives, birth attendants, and nurses) exhaustively recruited from 36 health facilities selected by convenience sampling. Workload was measured via the National Aeronautics and Space Administration Task Load Index (NASA-TLX) tool. Data were collected through face-to-face administration of a structured questionnaire at the end of participants’ work shifts. Descriptive and inferential statistical analyses were performed via STATA version 18. Measures of central tendency, including means and medians, were calculated. The Mann–Whitney and Kruskal–Wallis tests were applied to compare Raw-TLX scores across groups. Spearman’s correlation was used to assess the associations between the Raw-TLX dimensions and the overall score. Multiple linear regression with robust standard errors was conducted to identify factors independently associated with Raw-TLX. Statistical significance was set at p < 0.05. Results The overall median workload score (raw-TLX) was 69.2% (IQR 7.5), indicating a high workload. The mean scores for each workload dimension were also high: mental demand, 67.9 ± 8.7; physical demand, 69.0 ± 10.6; temporal demand, 70.5 ± 8.5; performance, 68.9 ± 10.1; effort, 67.1 ± 9.6; and frustration, 68.4 ± 6.1. The Spearman correlations between the Raw-TLX dimensions and the overall score were statistically significant (p < 0.001), with the highest coefficients observed for frustration (ρ = 0.73), effort (ρ = 0.70), and performance (ρ = 0.67). Older age (≥ 60 years) was significantly associated with increased Raw-TLX scores (B = 0.486; p = 0.0000). Conclusions Healthcare providers experience a globally high workload in the context of free maternity care policy, which is largely explained by advanced age. Workforce rejuvenation could help mitigate the effects of age-related workload burden. Additionally, integrating the NASA-TLX tool into health workforce management policies could enable routine monitoring of staff workload and support timely responses to excessive workload. Perceived workload Healthcare providers Free maternity care NASA-TLX Figures Figure 1 Introduction The Democratic Republic of the Congo (DRC) continues to face a high burden of maternal and neonatal mortality. According to the 2023–2024 Demographic and Health Survey, the neonatal mortality rate was estimated at 24 deaths per 1,000 live births, whereas the maternal mortality ratio reached 746 deaths per 100,000 live births—far above the Sustainable Development Goal target of 70 deaths per 100,000 live births [ 1 ]. The most frequent causes of maternal mortality include direct obstetric complications such as post-partum hemorrhage, uterine rupture, hypertensive disorders of pregnancy, puerperal infections, and unsafe abortions [ 2 ]. These complications are often exacerbated by limited access to quality antenatal, intrapartum, and postnatal care, contributing to persistently high maternal and neonatal mortality rates [ 3 ]. In this context, midwives constitute the main workforce in maternal health services and play a central role in reducing maternal and neonatal mortality [ 4 ]. In the DRC, midwives undergo three years of training at higher institutes of medical technology. In addition, certain A2-level nurses may access obstetric practice after completing a two-year retraining program to work as midwives. Their training, aligned with World Health Organization (WHO) and Ministry of Health guidelines, covers the management of pregnancy, childbirth, and the postpartum period, as well as the prevention and management of obstetric complications [ 5 ]. Adequate availability and distribution of these professionals within health facilities are essential for ensuring safe, high-quality maternal services [ 6 ]. However, the DRC faces a significant shortage of qualified personnel. In 2022, the country had only 2,734 midwives for 14,827 health facilities and 7,472 health posts [ 7 ]. In response to this shortage, other healthcare professionals, including nurses trained in general or pediatric care, sometimes assist in deliveries, although their obstetric skills are often limited. Furthermore, traditional birth attendants, commonly referred to as “matrones,” frequently practice without formal qualifications, particularly in rural areas. To improve access to and the quality of maternal health services, the Congolese government introduced a policy of free maternal and neonatal care, aiming to reduce financial barriers and promote equitable access to quality health services [ 8 ]. While this policy can improve service coverage and contribute to reducing maternal mortality, several studies indicate that it is often accompanied by an increase in service demand, which may lead to workload overload for an already insufficient workforce [ 6 , 9 , 10 ]. A study conducted in Kinshasa revealed that the policy significantly increased attendance at maternal health services [ 11 ]. Moreover, a recent assessment using the Workload Indicators of Staffing Need (WISN) tool revealed substantial workload overload in health facilities, with nearly two-thirds (61.5%) operating with only 44.8% of the required staff [ 12 ]. Such overload can result in occupational stress, burnout, and decreased performance among healthcare providers, potentially affecting the quality and safety of maternal care [ 13 – 15 ]. It may also undermine progress toward universal health coverage in the country [ 16 ]. Despite these concerns, few studies have assessed the perceived workload of healthcare providers in this specific context in the DRC. Workload is an important determinant of professional performance and care quality. It results from the interaction between task demands, the skills and perceptions of healthcare providers, and the organizational conditions in which they operate [ 17 , 18 ]. Influenced by job demands, the organizational environment, psychological factors, and the cognitive and physical capacities of providers [ 19 , 20 ], excessive workloads can lead to psychological stress, cognitive fatigue, reduced quality of care, and increased risk of burnout among healthcare professionals [ 14 ]. In this context, understanding the perceived workload of healthcare providers is essential for informing human resource management policies, adjusting staffing levels, and supporting professional performance to ensure safe and high-quality maternal care. The present study therefore aimed to assess the perceived workload of healthcare providers in the context of free maternal care in the DRC and to identify factors associated with this workload. Methods Study setting, design, and sampling This study was conducted in the city of Kananga, the capital of Kasaï Central Province, DRC. The study sites included all healthcare facilities—general referral hospitals (GRHs), secondary hospitals (SHs), and health centers (HCs)—located in four health zones benefiting from the free maternity care policy. Facilities were selected via a convenience sampling approach on the basis of their implementation of the free maternity policy and their geographic accessibility (Fig. 1 ). A total of 36 health facilities were included: five GRHs, one SH, and thirty-one HCs. An analytical cross-sectional study was conducted from December 26, 2025, to January 2026. The target population included all healthcare professionals assigned to the maternity wards of the selected facilities who provide maternal health services. The study focused primarily on midwives, birth attendants, hospital nurses, and other healthcare professionals trained in emergency neonatal care (ENC). In the DRC, these personnel typically graduate from higher education or university institutions or from medical teaching institutes after a three-year program, leading to either a graduat under the previous system or a bachelor’s degree in the current LMD system. An exhaustive sampling strategy was applied to include all eligible participants encountered in the field during data collection, according to the inclusion criteria detailed in Fig. 1 . Data collection instrument Data were collected using a structured questionnaire composed of two sections. The first section included sociodemographic variables developed by the research team, such as age, sex, marital status, years of service, and work schedule. An English version of this section is provided as Supplementary File 1. The second section assessed perceived workload using the National Aeronautics and Space Administration Task Load Index (NASA-TLX) [ 21 , 22 ]. The NASA-TLX provides a subjective assessment of mental workload and consists of six items that capture the demands required to perform tasks in professional settings (Table 1 ) [ 23 ]. The dimensions mental demand, physical demand, and temporal demand reflect the demands imposed on the participant, whereas performance, effort, and frustration focus on the participant’s interaction with the task. Each dimension was rated on a 20-point scale (with intervals of 5), ranging from “very low” to “very high,” except for the performance dimension, which ranged from “perfect” to “failure.” Scores from the six dimensions were combined to calculate an overall workload score ( raw -TLX), which was transformed to a 0–100 scale [ 24 ]. The French version of the NASA-TLX was used, the reliability of which was confirmed in a study assessing its psychometric properties among 28 healthcare professionals [ 18 ]. Since the aim of this study was to evaluate the overall workload rather than the task-specific workload, the unweighted version ( raw -TLX) was used. This approach is commonly adopted in the literature, particularly when professional activities are multiple, overlapping, and difficult to isolate, as is the case for the healthcare workers surveyed in this study [ 23 , 25 – 27 ]. Table 1 NASA-TLX Questionnaire Items and Scale TLX Dimension Questions Scale Mental Demand How mentally demanding was the task? How much mental activity did the task require (thinking, deciding, calculating, remembering, observing, searching, etc.)? 1 = low, 100 = high Physical Demand How physically demanding was the task? How much physical activity did the task require (e.g., pushing, pulling, turning, controlling, activating, etc.)? 1 = low, 100 = high Temporal Demand How fast-paced or hurried was the task? 1 = low, 100 = high Overall Performance How much effort did you have to put in to achieve your level of performance? 1 = low, 100 = high Effort How much effort did you have to put in to achieve your level of performance? 1 = low, 100 = high Frustration How insecure, discouraged, irritated, stressed, or annoyed did you feel regarding your work, as opposed to secure, satisfied, content, relaxed, and at ease? 1 = low, 100 = high This table represents the NASA-TLX questionnaire that was used to measure workload in our study. For each question, a slide bar allowed the participant to rate his or her subjective perception of workload from 1 to 100; TLX: Task Load Index Data collection Data were collected through face-to-face administration of a structured questionnaire to targeted healthcare workers from December 26, 2025, to January 20, 2026. Data collection was conducted by 10 carefully recruited field investigators who received two days of training on the use of the data collection tool and standardized questionnaire administration procedures. After administrative approval was obtained from the management of the participating healthcare facilities, participants were identified according to the inclusion criteria. The questionnaire was administered in paper format at the end of the participants’ work shifts. Healthcare workers completed the questionnaire after their work shift (day or night). The paper format was preferred due to the work context, characterized by limited access to digital tools (unstable internet connections and restricted availability of suitable devices), which made electronic administration challenging. The questionnaire was distributed after the investigators explained the study objectives and provided instructions on how to complete it. The investigators ensured that the responses were complete and provided clarifications when necessary. Once the questionnaires were completed, the data were entered into the KoBoCollect mobile application. A quality control procedure was conducted before submission of the forms, including verification of the consistency between the information recorded on the paper questionnaires and the digital entries. Data analysis Statistical analyses were performed via STATA 18. Both descriptive and inferential statistics were employed. Age and professional experience were categorized according to the regulatory thresholds for retirement eligibility in the public sector of the DRC (≤ 60 years vs. ≥ 60 years; ≤ 30 years vs. ≥ 30 years) to estimate the proportion of healthcare workers likely to retire soon in the context of a workforce shortage. Workload was assessed via the Raw-TLX of the NASA-TLX, with the unweighted mean of the six dimensions used to calculate the overall perceived workload score (Raw-TLX). The Raw-TLX is widely used in studies on healthcare professionals’ mental workload and provides a valid global index even without pairwise weighting, simplifying administration while maintaining measurement validity [ 28 , 29 ]. After the participants rated each of the six dimensions on a 20-point bipolar scale, the total workload score was calculated as the mean of the six scores multiplied by 5, transforming the score onto a 0–100 scale, where higher values reflect greater mental workload [ 30 ]. For descriptive purposes, overall workload was classified on the basis of the Raw-TLX score as follows: 0–20% = very low; 20–40% = low; 40–60% = moderate; 60–80% = high; and 80–100% = very high. The normality of the quantitative variables was assessed via the Shapiro–Wilk test. Quantitative variables were summarized as the means and standard deviations when normally distributed or as medians and interquartile ranges (IQRs) when not normally distributed. Categorical variables are reported as counts and percentages. Since the literature is inconsistent regarding the statistical description of Raw-TLX scores—some studies report means ± Standars deviations (SDs), whereas others report medians and interquartile rangs (IQRs)—both approaches were used in this study to facilitate comparisons with previous findings. Comparisons of Raw-TLX scores across participants’ sociodemographic characteristics were conducted via the Mann–Whitney test for variables with two categories and the Kruskal–Wallis test for variables with more than two categories. Spearman correlation was used to assess associations between the overall Raw-TLX score, its individual dimensions, age, and professional experience. Finally, multiple linear regression analysis with robust standard errors was performed to identify sociodemographic factors associated with the Raw-TLX score. Workload, as measured by the Raw-TLX, served as the dependent variable, whereas sociodemographic characteristics (age, sex, professional experience, etc.) were the independent variables. Statistical significance was set at p < 0.05. Results In this study, we analyzed data from 129 of the 134 healthcare professionals who completed the questionnaire, as four questionnaires were excluded because of incomplete data. In terms of gender distribution, 87.6% of the respondents were women. The median age of the participants was 40 years (IQR: 22 years). In addition, most respondents were midwives (44.2%), and the majority of them (63.6%) had a secondary-level education. The median length of professional experience was 12 years (IQR: 19 years). More than half of the participants (52.7%) were working on duty shifts (Table 2 ). Table 2 Sociodemographic characteristics of the participants Variables Categories Min Max Median IQR n (%) Age (years) ≤ 60 years 20 74 39 22 115 (89.2) > 60 years 14 (10.8) Sex Female – – – – 113 (87.6) Male – – – – 16 (12.4) Marital status Single – – – – 8 (6.2) Married – – – – 112 (86.8) Widowed – – – – 9 (7.0) Educational level Secondary – – – – 44 (34.1) Higher/University – – – – 85 (65.9) Training specialty Midwife – – – – 51 (39.5) Birth attendant – – – – 28 (21.7) Nurse – – – – 50 (38.8) Years of professional experience ≤ 30 years 25 55 12 18 6 (4.7) > 30 years 7 (5.4) Work shift Night – – – – 44 (34.1) Day – – – – 85 (65.9) As shown in Table 3 , the mean Raw-TLX scores indicate a high workload across all NASA-TLX dimensions considered. The mental demand (67.9 ± 8.7), physical demand (69.0 ± 10.6), and temporal demand (70.5 ± 8.5) had the highest mean scores. Performance (68.9 ± 10.1), effort (67.1 ± 9.6), and frustration (68.4 ± 6.1) had slightly lower mean scores. Overall, healthcare workers’ workload was moderate, with a median score of 68.4 (IQR: 6.1). Table 3 Descriptive Statistics of Workload Dimension Scores (Raw-TLX) Workload dimensions Median (IQR) Mean ± SD Min Max Workload level Mental demand 70.0 (60.0–75.0) 67.9 ± 8.7 45 90 High Physical demand 70.0 (65.0–75.0) 69.0 ± 10.6 40 90 High Temporal demand 70.0 (65.0–75.0) 70.5 ± 8.5 50 90 High Performance 70.0 (60.0–75.0) 68.5 ± 10.1 30 85 High Effort 70.0 (60.0–75.0) 67.1 ± 8.6 40 85 High Frustration 65.0 (60.0–75.0) 67.1 ± 9.6 40 90 High Total workload 69.2 (65.0–72.5) 68.4 ± 6.1 44.2 80 High This table presents the mean values of the Raw-TLX and its six subscales. Both means and medians were reported to facilitate comparisons with the literature. The overall workload score (raw-TLX) was compared across the participants’ sociodemographic characteristics (Table 4 ). Healthcare workers aged over 60 years had a median score of 73.7 (IQR: 2.5), whereas those aged 60 years or younger had a median score of 66.6 (IQR: 7.4); this difference was statistically significant (p = 0.0001). Similarly, participants with more than 30 years of professional experience had a greater overall workload, with a median score of 73.3 (IQR: 2.5), than did those with 30 years of experience or less, with a median score of 66.6 (IQR: 5.8); the difference was statistically significant (p < 0.0001). With respect to marital status, the median overall workload score ranged from 64.6 (IQR: 4.4) among single participants to 72.5 (IQR: 4.1) among widowed participants, with a statistically significant difference (p = 0.0304). No statistically significant differences were observed for the other sociodemographic variables examined. Table 4 Comparison of overall Raw-TLX scores according to participants’ sociodemographic characteristics Variables Categories n Overall Raw-TLX Median (Q1–Q3) p value Age (years) ≤ 60 years 115 68.3 (64.2–71.7) 0.001 > 60 years 14 75.8 (73.3–77.5) Sex Female 113 69.2 (65.0–72.5) 0.481 Male 16 66.7 (64.6–72.1) Marital status Single 8 68.3 (63.3–71.7) 0.001 Married 112 71.7 (68.3–73.8) Widowed 9 68.8 (65.0–71.7) Educational level Secondary 44 70.8 (68.3–73.8) 0.017 Higher/University 85 67.5 (64.2–71.7) Training specialty Midwife 51 68.3 (63.3–71.7) 0.091 Birth attendant 28 71.7 (68.3–73.8) Nurse 50 68.8 (65.0–71.7) Years of experience (years) ≤ 30 years 111 68.3 (64.2–71.7) 0.001 > 30 years 18 75.0 (72.5–76.7) Work shift Night 44 67.9 (63.3–71.7) 0.068 Day 85 70.0 (65.8–73.3) Note : ≤: less than or equal to; >: greater than; p < 0.05 indicates statistical significance. Spearman correlations between the perceived workload dimensions, the overall Raw-TLX score, age, and professional experience are presented in Table 5 . The overall workload score was significantly correlated with all six dimensions (p < 0.001), with the strongest correlations observed for frustration (ρ = 0.73), effort (ρ = 0.70), and performance (ρ = 0.67). With respect to age, significant correlations were found with mental demand (p = 0.046), physical demand (p < 0.001), temporal demand (p < 0.001), and the overall Raw-TLX score (p < 0.001). For professional experience, significant correlations were observed with physical demand (p < 0.001), temporal demand (p < 0.001), and the overall Raw-TLX score (p = 0.0001). No significant correlations were found with the other dimensions. Table 5 Spearman correlations between workload dimensions, overall score, age, and years of experience of the participants NASA-TLX Dimensions Overall Raw-TLX ρ (p) Age ρ (p) Years of experience ρ (p) Mental demand 0.42 (0.001) 0.22 (0.011) 0.26 (0.003) Physical demand 0.69 (0.001) 0.56 (0.001) 0.54 (0.001) Temporal demand 0.62 (0.001) 0.53 (0.001) 0.48 (0.001) Performance 0.70 (0.001) 0.32 (0.001) 0.28 (0.002) Effort 0.74 (0.001) 0.30 (0.001) 0.23 (0.009) Frustration 0.73 (0.001) 0.18 (0.043) 0.16 (0.076) Overall Raw-TLX score 1.00 0.55 (< 0.001) 0.50 (0.001) Note: TLX = task load index; ρ = Spearman correlation coefficient; p < 0.05 indicates statistical significance. The results of the multiple linear regression analysis (Table 6 ) indicate that, among the variables studied, only age was significantly associated with the overall workload score (B = 0.232; p < 0.001; standardized β = 0.518). Table 6 Multiple linear regression analysis of factors associated with the Raw-TLX workload score Unstandardized Coefficients B Std. Error Sig. 95% Confidence Interval for B Standardized Coefficients Beta Lower Bound Upper Bound Constant 55.88 2.80 0.000 50.34 61.42 – Sex 0.486 1.24 0.696 −1.97 2.95 0.026 Age 0.232 0.041 0.000 0.151 0.312 0.518 Marital status 1.139 1.08 0.295 −1.01 3.29 0.068 Educational level −0.001 0.965 0.999 −1.91 1.91 −0.00008 Training specialty −0.257 0.497 0.607 −1.24 0.73 −0.037 Work shift 0.312 0.930 0.738 −1.53 2.15 0.024 Note : Unstandardized coefficients indicate how much the Raw-TLX score changes for a one-unit increase in the predictor variable listed in the first column. Overall model F(6,122) = 14.96; p < 0.001; adjusted R² = 30.5%; standard error (SE) = 5.2%. Discussion In the present study, workload was assessed among healthcare providers involved in delivering free maternal health services in Kananga. In accordance with the requirements set by technical and financial partners and implemented by the Ministry of Public Health, maternal health services in this context should be provided primarily by midwives, who are trained in higher education institutions focused on university-level education, scientific research, and innovation and certified after three years of training aligned with the recommendations of the International Confederation of Midwives [ 5 ]. Owing to the persistent shortage of qualified personnel in the DRC, other categories of providers, including nurses, are trained and retrained in ENC to support midwives in delivering maternal health services. In our study, 39.5% of the participants were midwives, 38.8% were nurses trained in ENC, and 21.7% were birth attendants retrained in ENC. This distribution reflects the structural challenges of the Congolese health system in ensuring an adequate supply of qualified personnel to provide high-quality maternal health services. However, the availability of skilled providers is a critical determinant for improving care quality and reducing maternal mortality [ 6 ]. Overall, the Raw-TLX results indicated that healthcare providers on duty experienced high workloads. The overall median score was 67.5 (IQR: 8.3), corresponding to a globally high workload. These findings are consistent with reports from multiple studies among healthcare professionals, including hospital nurses, where Raw-TLX also highlighted high levels of mental workload [ 21 , 23 , 26 , 29 , 31 , 32 ]. This overload may be explained by the increased demand for maternal health services not accompanied by sufficient human resources, as documented in this region [ 11 , 12 ]. The study also reported that all Raw-TLX dimensions were positively and significantly correlated with the overall score (p < 0.001), confirming the tool’s internal consistency in this context. The “frustration,” “effort,” and “performance” dimensions contributed the most to the overall score (correlation coefficients: 0.73, 0.70, and 0.67, respectively), suggesting that perceived overload is strongly related to psychological demands and the intensity of engagement required to perform tasks. Additionally, the age and seniority of the providers were more strongly correlated with the physical and temporal workload dimensions than with the other components. The overall workload score was significantly higher among providers aged over 60 years than among their younger colleagues (p = 0.0001). This observation may be explained by the progressive decline in physical capacity and recovery associated with aging, which renders professional demands more challenging [ 33 ]. Similarly, providers with more than 30 years of experience had higher workload scores than did their less experienced colleagues (p < 0.001). This could be attributed to their assumption of greater organizational and clinical responsibilities, increased involvement in complex decision-making, and active participation in mentoring and supervising new staff [ 34 ]. Higher workload levels were also observed among widowed providers; however, this association should be interpreted cautiously, as it may reflect psychosocial and personal vulnerability factors interacting with professional demands. Our study further indicated that age was the only significant predictor of workload. These findings suggest that the physiological and functional changes associated with aging may play a decisive role in the perception and management of professional demands. Aging is generally accompanied by a gradual decline in certain physical capacities, including endurance, aerobic capacity, and recovery speed, after prolonged exertion. Such changes can make professional tasks more demanding for older providers, particularly in sectors with high physical and organizational requirements. A review of the work capacity of aging workers, for example, reported an average reduction of approximately 20% in physical capacity between the ages of 40 and 60 years due to musculoskeletal and cardiovascular declines, potentially affecting the ability to meet work demands and increasing perceived workload [ 35 ]. In the healthcare sector, these effects may be particularly pronounced. Hospital activities, notably maternal health services, often involve emergencies, irregular schedules, constant vigilance, and significant decision-making responsibilities. These organizational and emotional demands can amplify the impact of age-related changes on workload perception. Several studies among healthcare professionals indicate that work capacity tends to decline with age, especially in professions with high physical demands, such as nursing or nursing aides [ 36 ]. Research on caregiver work capacity further shows that age is associated with decreased physical capacity and increased vulnerability to burnout when professional resources are insufficient [ 37 ]. Moreover, the influence of age on workload may also reflect the professional roles of more experienced providers. In contexts marked by health workforce shortages, these professionals are often more heavily reliant upon due to their clinical expertise and supervisory responsibilities. This accumulation of responsibilities can increase the perceived workload independently of the physical demands of the position [ 20 ]. This effect may be exacerbated by task redistribution caused by personnel shortages, which intensifies the workload for existing staff, particularly the most experienced staff. Nevertheless, the relationship between age and workload remains complex and is highly dependent on the organizational context, working conditions, and available resources. Some studies emphasize that, despite declines in certain physical capacities, older workers may compensate through greater experience, procedural mastery, and efficiency in managing professional tasks. In well-resourced and supportive work environments, these acquired skills may help maintain work capacity despite aging [ 37 ]. Thus, age emerges as an important determinant of perceived workload, but its influence results from complex interactions among individual capacity, professional experience, and the organizational characteristics of the workplace. These findings highlight the importance of incorporating demographic factors into health human resource management policies. Consideration of the workforce age structure, adaptation of working conditions, and implementation of organizational support strategies could help preserve work capacity and reduce occupational overload, particularly in services with high physical, emotional, and decision-making demands. Notably, although age was the only significant factor in our model, it alone cannot explain the high workload. Perceived workload results from a complex interplay of individual, organizational, and contextual factors, such as task distribution, clinical responsibilities, staffing levels, and psychosocial conditions [ 20 , 23 ]. Our model may thus have been limited in capturing the full range of determinants, underscoring the need for more comprehensive analytical approaches. In the context of maternal health services, these findings are particularly important. As pillars of the maternal health system, overburdened healthcare providers face an increased risk of burnout, decreased job satisfaction, and eventual disengagement [ 13 , 27 , 32 , 38 , 39 ]. These factors can affect the quality and continuity of care, including reduced time spent with patients, diminished capacity to manage obstetric complications, and overall degradation of service performance. In the DRC, where maternal mortality remains high, this situation poses a major challenge to the health system and may compromise progress toward Universal Health Coverage [ 16 ]. Free maternal care, as an initial component of this strategy, aims to respond rapidly to health needs and save lives, but it may also increase pressure on already limited human resources [ 12 ]. These results underscore the need for sustainable workload management strategies. Health authorities could consider implementing gradual workforce rejuvenation, accompanied by enhanced continuous training and professional integration of new recruits. The findings also provide evidence to guide health human resource policy decisions. The Ministry of Health might adopt structured workload management strategies, including regular monitoring of staff workload. Integration of assessment tools such as the NASA-TLX or the WISN [ 12 ] into human resource planning and management processes could help anticipate overload situations and implement timely, appropriate interventions. Study Limitations This study had several notable limitations that may have introduced bias in the results. First, a convenience sample was used, which limits the generalizability of the findings to the broader population of healthcare professionals. Second, the study relied exclusively on self-reported data obtained via the Raw-TLX questionnaire, which may introduce social desirability bias and affect response accuracy, particularly for dimensions perceived as sensitive, such as frustration or effort. Third, although all Raw-TLX dimensions were correlated with the overall score, some correlations with age and seniority were weak or nonsignificant, suggesting that perceived workload may vary according to individual factors not measured in this study. Therefore, the results should be interpreted with caution. Another limitation concerns the interpretation of Raw-TLX scores as classified in this study. To date, the literature has not established a fixed threshold defining workload as “too low” or “too high,” despite decades of research. This lack of a clear cutoff may be due to individual differences in coping strategies, knowledge, and experience, which influence how tasks at the extremes of the workload continuum are perceived [ 40 ]. Although the multiple linear regression with robust standard errors was statistically significant, the root mean square error (RMSE) of 5.20 indicates that individual predictions may deviate on average by approximately ± 5 points from the observed scores, reflecting unexplained variability in the model. With an R² of 0.305, only 30% of the variance in the total score is explained, leaving the majority of the variance potentially attributable to unmeasured or unobserved factors. Certain theoretically relevant sociodemographic variables—such as sex, marital status, education level, field of study, and work schedule—did not have significant effects and contributed little to predicting Raw-TLX scores, limiting the precision of estimates for individual participants. Nonetheless, the model remains useful for assessing overall trends and the relative effects of variables, with age emerging as the most influential factor. Despite these limitations, this study represents one of the first efforts to explore this domain in the context of free maternal health services. Future research could consider combining subjective and objective workload measures, as well as including larger and more diverse samples, to better understand healthcare providers’ workload in this setting. Conclusions This study explored the perceived workload of healthcare professionals in the context of free maternal health services. The results indicate that these professionals experience a high workload, which is associated primarily with age. These findings highlight the importance of incorporating the assessment of perceived workload into the planning and management of free maternity services. They also underscore the need to develop targeted strategies to support healthcare workers’ well-being and ensure the delivery of high-quality care. Such strategies may include, for example, workforce rejuvenation and regular retraining of existing staff to better manage the current workload. Abbreviations ANC Antenatal Consultation DHS Demographic and Health Survey DRC Democratic Republic of the Congo GRH General Referral Hospital HC Health Center HF Health Facility UHC Universal Health Coverage HP Health Post HRH Human Resources for Health HZ Health Zone SH Secondary Hospital NASA-TLX National Aeronautics and Space Administration Task Load Index IQR Interquartile Ranges Declarations Ethics approval and consent to participate All ethical considerations were strictly observed in accordance with the principles outlined in the Declaration of Helsinki. The study received ethical approval from the Kinshasa School of Public Health Ethics Committee (reference no. ESP/CE/064B/2024). Additionally, verbal authorization was obtained from the administrators of the selected healthcare facilities in accordance with each institution’s internal procedures, and informed verbal consent was obtained from all participants prior to the interviews. Clinical trial number Not applicable Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Health Organization Management — Higher Institute of Medical Techniques of Kananga, Student at the Kinshasa School of Public Health, Kananga, DRC. 2 Faculty of Medicine, University of Notre-Dame of Kasayi 3 Department of Health Management and Policy, Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of Congo Funding This study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon prior request. Author Contribution PNM designed the study. PNM, DPM, JD, and ABN coordinated the data collection and study activities. EMM provided overall supervision of the study. PNM analyzed the data and drafted the initial manuscript. All the authors read and approved the final version of the manuscript. Acknowledgement The researchers thank all the individuals who generously contributed to the completion of this study. Data Availability The data that support the findings of this study are available from the corresponding author upon prior request. References Institut National de Statistique. École de Santé Publique de Kinshasa. Enquête Démographique et de Santé (EDS-RDC III) 2023–2024: Rapport final. Ministère du Plan et Suivi de la Mise en oeuvre de la Révolution de la Modernité. Kinshasa (DRC); 2025. Bin I, Ramazani E, Decap S, Ntela M, Ahouah M, Ishoso DK, et al. Maternal mortality study in the Eastern Democratic Republic of the congo. BMC Pregnancy Childbirth. 2022;22(1):1–14. https://doi.org/10.1186/s12884-022-04783–z . Batu PM, Ndokabilya E, Lembebu JC, Ngaboyeka G, Bigirinama R, Hermans MP, et al. Maternal mortality in Eastern Democratic Republic of congo: a 10-year multizonal institutional death review. BMC Public Health. 2024;24:19804. https://doi.org/10.1186/s12889-024-19804-z . Fikre R, Gubbels J, Teklesilasie W, Gerards S. Effectiveness of midwifery-led care on pregnancy outcomes in low- and middle-income countries: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2023;23:56664. https://doi.org/10.1186/s12884-023-05664-9 . International Confederation of Midwives (ICM). Normes de l’International Confederation of Midwives (ICM) pour la formation des sages-femmes. 2021;1–12. World Health Organization. Global strategy on human resources for health: workforce 2030. Geneva: WHO; 2016. Ministère de la Santé Publique. Hygiène et Prévention. Annuaire statistique des ressources humaines de La santé 2022. Direction des Ressources Humaines: Kinshasa (DRC); 2024. de Unité. Gestion du programme de Développement du Système de Santé (UG-PDSS). Revue Annuelle 2022. UG-PDSS: Kinshasa (DRC);; 2022. United Nations Population Fund. The state of the world’s midwifery 2022. New York: UNFPA; 2022. Kouanda S, Ly A, Bonnet E, Ridde V. La charge de travail du personnel de santé face à la gratuité des soins au Burkina Faso. Afr Contemp. 2012;243:104–5. https://doi.org/10.3917/afco.243.0104 . Ecole de Santé Publique de Kinshasa (ESPK). Institut National de Santé Publique (INSP). Évaluation de l’effet de la politique de la gratuité des services d’accouchement sur l’accès au service de maternité dans la Ville de Kinshasa. Kinshasa: ESPK/INSP; 2025. Nkolamoyo Musungula P, Kalengo Nsomue C, Esanga Longomo E, Muelu Mikobi P, Djongesongo Djamba B, Mafuta Musalu E. Midwives workload in the context of free maternal healthcare: a cross-sectional study based on the Workload Indicators of Staffing Needs (WISN) method in primary healthcare facilities in Kananga, Democratic Republic of the Congo. BMC Health Serv Res. 2025;25(1):1468. https://doi.org/10.1186/s12913-025-13656-y . PMID: 41239484; PMCID: PMC12619488. Sidhu R, Su B, Shapiro KR, Stoll K. Prevalence of and factors associated with burnout in midwifery: A scoping review. Eur J Midwifery. 2020;4:4. 10.18332/ejm/115983 . PMID: 33537606; PMCID: PMC7839164. Rouleau D, Fournier P, Philibert A, Mbengue B, Dumont A. The effects of midwives’ job satisfaction on burnout, intention to quit and turnover: a longitudinal study in Senegal. Hum Resour Health. 2012;10:9. h ttps://doi.org/10.1186/1478-4491-10-9 . PMID: 22546053. Acker CE. Higher mental workload is associated with poorer laparoscopic performance as measured by the NASA-TLX tool. Surg Innov. 2010;5(3):267–71. https://doi.org/10.1097/SIH.0b013e3181e3f329 . Kapiteni W, Kadima JN. Women’s health and wellness: insight into audit reports on the causes of maternal deaths in poor health settings – the case of North Kivu Province in Democratic Republic of Congo. J Women’s Health Care. 2018;4:79. https://doi.org/10.23937/2474-1353/1510079 . Hart SG, Staveland LE. NASA Task Load Index (TLX). NASA Ames Research Center; 1988. Technical Report. Cegarra J, Morgado N. Étude des propriétés de la version francophone du NASA-TLX. 2016. Lützerath J, Bleier H, Stassen G, Schaller A. Influencing factors on the health of nurses—a regression analysis considering individual and organizational determinants in Germany. BMC Health Serv Res. 2023;23:1–11. https://doi.org/10.1186/s12913-023-09106-2 . Nagle E, Griskevica I, Rajevska O, Ivanovs A, Mihailova S, Skruzkalne I. Factors affecting healthcare workers burnout and their conceptual models: scoping review. BMC Psychol 2024;12. https://doi.org/10.1186/s40359-024-02130-9 Hart SG, Staveland LE. Human mental workload. In: Hancock PA, Meshkati N, editors. Human Mental Workload. Amsterdam: Elsevier Science; 1988. pp. 85–194. Hart SG. NASA-Task Load Index (NASA-TLX); 20 years later. Proc Hum Factors Ergon Soc Annu Meeting. 2006;50:904–8. https://doi.org/10.1177/154193120605000909 . Keunecke JG, Gall C, Birkholz T, Moritz A, Eiche C, Prottengeier J. Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study. BMC Health Serv Res. 2019;19:4638. https://doi.org/10.1186/s12913-019-4638-4 . Frcpc RM, Frcpc MR, Anderson J, Edin F. Task Load Index as a tool to evaluate the learning curve for endoscopy training. 2014;28:155–60. Tubbs-Cooley HL, Mara CA, Carle AC, Gurses AP. The NASA Task Load Index as a measure of overall workload among neonatal, pediatric and adult intensive care nurses. Intensive Crit Care Nurs. 2018;47:1–6. https://doi.org/10.1016/j.iccn.2018.01.004 . Nur I, Iskandar H, Rf A. The measurement of nurses’ mental workload using NASA-TLX method (a case study). Int J Nurs Sci. 2020;7(1):60–3. Shoja E, Aghamohammadi V, Bazyar H, Moghaddam HR, Nasiri K, Dashti M, et al. COVID-19 effects on the workload of Iranian healthcare workers. BMC Public Health. 2020;20:9743. https://doi.org/10.1186/s12889-020-09743-w . Said S, Gozdzik M, Roche TR, Braun J, Rössler J, Kaserer A, Spahn DR, Nöthiger CB, Tscholl DW. Validation of the Raw National Aeronautics and Space Administration Task Load Index (NASA-TLX) Questionnaire to Assess Perceived Workload in Patient Monitoring Tasks: Pooled Analysis Study Using Mixed Models. J Med Internet Res. 2020;22(9):e19472. 10.2196/19472 . PMID: 32780712; PMCID: PMC7506540. Zehnder EC, Law BHY, Schmölzer GM. Assessment of healthcare provider workload in neonatal resuscitation. Front Pediatr. 2020;8:598475. https://doi.org/10.3389/fped.2020.598475 . Habibi E, Taheri MR, Hasanzadeh A. Relationship between mental workload and musculoskeletal disorders among Alzahra Hospital nurses. Work. 2015;53(1):1–6. https://doi.org/10.3233/WOR-152067 . Sheffel A, Andrews KG, Conner R, Di Giorgio L, Evans DK, Gatti R, et al. Human resource challenges in health systems: evidence from 10 African countries. Health Policy Plan. 2024;39:693–709. https://doi.org/10.1093/heapol/czae034 . Jiang W, Wang Y, Zhang J, Song D, Pu C, Shan C. The impact of workload and traumatic stress on the presenteeism of midwives: the mediating effect of psychological detachment. Front Psychol. 2023;14:1195700. https://doi.org/10.3389/fpsyg.2023.1195700 . Ranchet M, Morgan JC, Akinwuntan AE, Devos H. Cognitive workload across the spectrum of cognitive impairments: a systematic review of physiological measures. Neurosci Biobehav Rev. 2017;80:516–37. https://doi.org/10.1016/j.neubiorev.2017.07.001 . Park S, Yoo J, Lee Y, DeGuzman PB, Kang MJ, Dykes PC, et al. Quantifying emergency department nursing workload at the task level using NASA-TLX: an exploratory descriptive study. Int Emerg Nurs. 2024;74:101424. https://doi.org/10.1016/j.ienj.2024.101424 . Kenny GP, Yardley JE, Martineau L, Jay O. Physical work capacity in older adults: implications for the aging worker. Am J Ind Med. 2008;51:610–25. https://doi.org/10.1002/ajim.20600 . Garzaro G, Clari M, Ciocan C, Albanesi B, Guidetti G, Dimonte V, et al. Physical health and work ability among healthcare workers: a cross-sectional study. Nurs Rep. 2022;12:259–69. https://doi.org/10.3390/nursrep12020026 . Hatch DJ, Freude G, Martus P, Rose U, Müller G, Potter GG. Age, burnout and physical and psychological work ability among nurses. Occup Med (Lond). 2018;68:246–54. https://doi.org/10.1093/occmed/kqy033 . Rostami F, Babaei-Pouya A, Teimori-Boghsani G. Mental workload and job satisfaction in healthcare workers: the moderating role of job control. Front Public Health. 2021;9:683388. https://doi.org/10.3389/fpubh.2021.683388 . Wang L, Huang Q, Zhang Y, Liu J, Chen C. The impact of perceived workload on nurse presenteeism and missed nursing care: the mediating role of emotional intelligence and occupational stress. 2025. Grier RA. (2015). How High is High? A Meta-Analysis of NASA-TLX Global Workload Scores. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 1727–1731. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9075820","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607002204,"identity":"c3d19f3a-86f5-463e-8586-7278ca1b4809","order_by":0,"name":"Paulin Nkolamoyo Musungula","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYHACxgMJDAwGDOwQnhyIOPCAgB6IFmYIxxgmgl8LA5KWxAYQiU+LOfsZgwMPd9wx5mdmfrrhY5tN+vywww+BttjJ6TZg12LZk2NwIPHMMzPJZjazmzPb0nI33k4zAGpJNjY7gF2LwQGQlrbDNgaHGcxu87Ydzt04OwGk5UDiNlxazr+BaWH/BtKSbjg7/QN+LTcgtpgZHOYB25IgL52D3xbLGc8KQFqMJZt5ym7OOJdmuEE6p+BAggFuv5jzJ298+LPtsGE/e/u2Gx/KbOTlZ6dv/vChwk4Op/dReIxsoADBFMejheEPA4N8A27Vo2AUjIJRMDIBALmhai+/fr4/AAAAAElFTkSuQmCC","orcid":"","institution":"Department of Health Organization Management — Higher Institute of Medical Techniques of Kananga","correspondingAuthor":true,"prefix":"","firstName":"Paulin","middleName":"Nkolamoyo","lastName":"Musungula","suffix":""},{"id":607002205,"identity":"6c61ea95-8425-4d63-9849-4d05cb8f4224","order_by":1,"name":"David Pamutena Mashingu","email":"","orcid":"","institution":"Department of Health Organization Management — Higher Institute of Medical Techniques of Kananga","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"Pamutena","lastName":"Mashingu","suffix":""},{"id":607002207,"identity":"12457de1-eb8a-43c9-904c-38ca919a31a4","order_by":2,"name":"Jean Claude Djulu Shamanga","email":"","orcid":"","institution":"Department of Health Organization Management — Higher Institute of Medical Techniques of Kananga","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"Claude Djulu","lastName":"Shamanga","suffix":""},{"id":607002208,"identity":"a6818145-325e-4191-809e-fc7e61b5aa96","order_by":3,"name":"Angel Bapedi Ngalamulume","email":"","orcid":"","institution":"Faculty of Medicine, University of Notre-Dame of Kasayi","correspondingAuthor":false,"prefix":"","firstName":"Angel","middleName":"Bapedi","lastName":"Ngalamulume","suffix":""},{"id":607002210,"identity":"7d0e9d5a-6c6a-42cf-bce1-1c50d7856436","order_by":4,"name":"Eric Mafuta Musalu","email":"","orcid":"","institution":"Department of Health Management and Policy, Kinshasa School of Public Health, University of Kinshasa","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"Mafuta","lastName":"Musalu","suffix":""}],"badges":[],"createdAt":"2026-03-09 17:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9075820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9075820/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104888274,"identity":"b3f83636-f603-4d07-a5e8-2001068d1634","added_by":"auto","created_at":"2026-03-18 10:14:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98000,"visible":true,"origin":"","legend":"\u003cp\u003eSampling plan of the surveyed health facilities and health workers\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9075820/v1/3ab6a7ee30692e51955523aa.png"},{"id":105034314,"identity":"cf90df54-85f4-4587-80d4-c5ca2843da90","added_by":"auto","created_at":"2026-03-20 07:23:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1133742,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9075820/v1/59746e1b-0140-4116-834c-977af2da4e58.pdf"},{"id":104888275,"identity":"177e12f0-5f20-4273-b380-e86afce4680d","added_by":"auto","created_at":"2026-03-18 10:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7619,"visible":true,"origin":"","legend":"","description":"","filename":"Questionnairesociodemographic.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9075820/v1/67f4890e9d0fcb1c1a7175e7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceived workload of healthcare providers delivering free maternal services: An analytical cross-sectional etudy based on the National Aeronautics and Space Administration Task Load Index (NASA-TLX) in primary health care facilities in Kananga, Democratic Republic of the Congo","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Democratic Republic of the Congo (DRC) continues to face a high burden of maternal and neonatal mortality. According to the 2023\u0026ndash;2024 Demographic and Health Survey, the neonatal mortality rate was estimated at 24 deaths per 1,000 live births, whereas the maternal mortality ratio reached 746 deaths per 100,000 live births\u0026mdash;far above the Sustainable Development Goal target of 70 deaths per 100,000 live births [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most frequent causes of maternal mortality include direct obstetric complications such as post-partum hemorrhage, uterine rupture, hypertensive disorders of pregnancy, puerperal infections, and unsafe abortions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These complications are often exacerbated by limited access to quality antenatal, intrapartum, and postnatal care, contributing to persistently high maternal and neonatal mortality rates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, midwives constitute the main workforce in maternal health services and play a central role in reducing maternal and neonatal mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the DRC, midwives undergo three years of training at higher institutes of medical technology. In addition, certain A2-level nurses may access obstetric practice after completing a two-year retraining program to work as midwives. Their training, aligned with World Health Organization (WHO) and Ministry of Health guidelines, covers the management of pregnancy, childbirth, and the postpartum period, as well as the prevention and management of obstetric complications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Adequate availability and distribution of these professionals within health facilities are essential for ensuring safe, high-quality maternal services [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the DRC faces a significant shortage of qualified personnel. In 2022, the country had only 2,734 midwives for 14,827 health facilities and 7,472 health posts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In response to this shortage, other healthcare professionals, including nurses trained in general or pediatric care, sometimes assist in deliveries, although their obstetric skills are often limited. Furthermore, traditional birth attendants, commonly referred to as \u0026ldquo;matrones,\u0026rdquo; frequently practice without formal qualifications, particularly in rural areas.\u003c/p\u003e \u003cp\u003eTo improve access to and the quality of maternal health services, the Congolese government introduced a policy of free maternal and neonatal care, aiming to reduce financial barriers and promote equitable access to quality health services [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While this policy can improve service coverage and contribute to reducing maternal mortality, several studies indicate that it is often accompanied by an increase in service demand, which may lead to workload overload for an already insufficient workforce [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A study conducted in Kinshasa revealed that the policy significantly increased attendance at maternal health services [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, a recent assessment using the Workload Indicators of Staffing Need (WISN) tool revealed substantial workload overload in health facilities, with nearly two-thirds (61.5%) operating with only 44.8% of the required staff [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Such overload can result in occupational stress, burnout, and decreased performance among healthcare providers, potentially affecting the quality and safety of maternal care [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It may also undermine progress toward universal health coverage in the country [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these concerns, few studies have assessed the perceived workload of healthcare providers in this specific context in the DRC. Workload is an important determinant of professional performance and care quality. It results from the interaction between task demands, the skills and perceptions of healthcare providers, and the organizational conditions in which they operate [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Influenced by job demands, the organizational environment, psychological factors, and the cognitive and physical capacities of providers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], excessive workloads can lead to psychological stress, cognitive fatigue, reduced quality of care, and increased risk of burnout among healthcare professionals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, understanding the perceived workload of healthcare providers is essential for informing human resource management policies, adjusting staffing levels, and supporting professional performance to ensure safe and high-quality maternal care. The present study therefore aimed to assess the perceived workload of healthcare providers in the context of free maternal care in the DRC and to identify factors associated with this workload.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting, design, and sampling\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study was conducted in the city of Kananga, the capital of Kasa\u0026iuml; Central Province, DRC. The study sites included all healthcare facilities\u0026mdash;general referral hospitals (GRHs), secondary hospitals (SHs), and health centers (HCs)\u0026mdash;located in four health zones benefiting from the free maternity care policy. Facilities were selected \u003cem\u003evia\u003c/em\u003e a convenience sampling approach on \u003cem\u003ethe basis of\u003c/em\u003e their implementation of the free maternity policy and their geographic accessibility (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 36 health facilities were included: five GRHs, one SH, and thirty-one HCs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn analytical cross-sectional study was conducted from December 26, 2025, to January 2026. The target population included all healthcare professionals assigned to the maternity wards of the selected facilities who provide maternal health services. The study \u003cem\u003efocused\u003c/em\u003e primarily \u003cem\u003eon\u003c/em\u003e midwives, birth attendants, hospital nurses, and other healthcare professionals trained in \u003cem\u003eemergency neonatal care\u003c/em\u003e (ENC). In the DRC, these personnel typically graduate from higher education or university institutions or from medical teaching institutes after a three-year program, leading to either a graduat under the previous system or a bachelor\u0026rsquo;s degree in the current LMD system. An exhaustive sampling strategy was applied to include all eligible participants encountered in the field during data collection, according to the inclusion criteria detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection instrument\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured questionnaire composed of two sections. The first section included sociodemographic variables developed by the research team, such as age, sex, marital status, years of service, and work schedule. An English version of this section is provided as Supplementary File 1. The second section assessed perceived workload using the National Aeronautics and Space Administration Task Load Index (NASA-TLX) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The NASA-TLX provides a subjective assessment of mental workload and consists of six items that capture the demands required to perform tasks in professional settings (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The dimensions mental demand, physical demand, and temporal demand reflect the demands imposed on the participant, \u003cem\u003ewhereas\u003c/em\u003e performance, effort, and frustration focus on the participant\u0026rsquo;s interaction with the task. Each dimension was rated on a 20-point scale (with intervals of 5), ranging from \u0026ldquo;very low\u0026rdquo; to \u0026ldquo;very high,\u0026rdquo; except for the performance dimension, which ranged from \u0026ldquo;perfect\u0026rdquo; to \u0026ldquo;failure.\u0026rdquo; Scores from the six dimensions were combined to calculate an overall workload score (\u003cem\u003eraw\u003c/em\u003e-TLX), \u003cem\u003ewhich was\u003c/em\u003e transformed to a 0\u0026ndash;100 scale [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The French version of the NASA-TLX was used, \u003cem\u003ethe\u003c/em\u003e reliability \u003cem\u003eof which was\u003c/em\u003e confirmed in a study assessing its psychometric properties among 28 healthcare professionals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Since the aim of this study was to evaluate \u003cem\u003ethe\u003c/em\u003e overall workload rather than \u003cem\u003ethe\u003c/em\u003e task-specific workload, the unweighted version (\u003cem\u003eraw\u003c/em\u003e-TLX) was used. This approach is commonly adopted in the literature, particularly when professional activities are multiple, overlapping, and difficult to isolate, as is the case for the healthcare workers surveyed in this study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\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\u003eNASA-TLX Questionnaire Items and Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLX Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eQuestions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMental Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow mentally demanding was the task? How much mental activity did the task require (thinking, deciding, calculating, remembering, observing, searching, etc.)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 100\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhysical Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow physically demanding was the task? How much physical activity did the task require (e.g., pushing, pulling, turning, controlling, activating, etc.)?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 100\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTemporal Demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow fast-paced or hurried was the task?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 100\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOverall Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow much effort did you have to put in to achieve your level of performance?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 100\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow much effort did you have to put in to achieve your level of performance?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 100\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFrustration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow insecure, discouraged, irritated, stressed, or annoyed did you feel regarding your work, as opposed to secure, satisfied, content, relaxed, and at ease?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 100\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThis table represents the NASA-TLX questionnaire that was used to measure workload in our study. For each question, a slide bar allowed the participant to rate his or her subjective perception of workload from 1 to 100; TLX: Task Load Index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected through face-to-face administration of a structured questionnaire to targeted healthcare workers from December 26, 2025, to January 20, 2026. Data collection was conducted by 10 carefully recruited field investigators who received two days of training on the use of the data collection tool and standardized questionnaire administration procedures.\u003c/p\u003e \u003cp\u003e After administrative approval was obtained from the management of the participating healthcare facilities, participants were identified according to the inclusion criteria. The questionnaire was administered in paper format at the end of the participants\u0026rsquo; work shifts. Healthcare workers completed the questionnaire after their work shift (day or night). The paper format was preferred due to the work context, characterized by limited access to digital tools (unstable internet connections and restricted availability of suitable devices), which made electronic administration challenging.\u003c/p\u003e \u003cp\u003eThe questionnaire was distributed after the investigators explained the study objectives and provided instructions on how to complete it. The investigators ensured that the responses were complete and provided clarifications when necessary. Once the questionnaires were completed, the data were entered into the KoBoCollect mobile application. A quality control procedure was conducted before submission of the forms, including verification of the consistency between the information recorded on the paper questionnaires and the digital entries.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed via STATA 18. Both descriptive and inferential statistics were employed. Age and professional experience were categorized according to the regulatory thresholds for retirement eligibility in the public sector of the DRC (\u0026le;\u0026thinsp;60 years vs. \u0026ge; 60 years; \u0026le; 30 years vs. \u0026ge; 30 years) to estimate the proportion of healthcare workers likely to retire soon in the context of a workforce shortage.\u003c/p\u003e \u003cp\u003eWorkload was assessed via the Raw-TLX of the NASA-TLX, with the unweighted mean of the six dimensions used to calculate the overall perceived workload score (Raw-TLX). The Raw-TLX is widely used in studies on healthcare professionals\u0026rsquo; mental workload and provides a valid global index even without pairwise weighting, simplifying administration while maintaining measurement validity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. After the participants rated each of the six dimensions on a 20-point bipolar scale, the total workload score was calculated as the mean of the six scores multiplied by 5, transforming the score onto a 0\u0026ndash;100 scale, where higher values reflect greater mental workload [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For descriptive purposes, overall workload was classified on the basis of the Raw-TLX score as follows: 0\u0026ndash;20% = very low; 20\u0026ndash;40% = low; 40\u0026ndash;60% = moderate; 60\u0026ndash;80% = high; and 80\u0026ndash;100% = very high.\u003c/p\u003e \u003cp\u003eThe normality of the quantitative variables was assessed via the Shapiro\u0026ndash;Wilk test. Quantitative variables were summarized as the means and standard deviations when normally distributed or as medians and interquartile ranges (IQRs) when not normally distributed. Categorical variables are reported as counts and percentages. Since the literature is inconsistent regarding the statistical description of Raw-TLX scores\u0026mdash;some studies report means\u0026thinsp;\u0026plusmn;\u0026thinsp;Standars deviations (SDs), whereas others report medians and interquartile rangs (IQRs)\u0026mdash;both approaches were used in this study to facilitate comparisons with previous findings.\u003c/p\u003e \u003cp\u003eComparisons of Raw-TLX scores across participants\u0026rsquo; sociodemographic characteristics were conducted via the Mann\u0026ndash;Whitney test for variables with two categories and the Kruskal\u0026ndash;Wallis test for variables with more than two categories. Spearman correlation was used to assess associations between the overall Raw-TLX score, its individual dimensions, age, and professional experience. Finally, multiple linear regression analysis with robust standard errors was performed to identify sociodemographic factors associated with the Raw-TLX score. Workload, as measured by the Raw-TLX, served as the dependent variable, whereas sociodemographic characteristics (age, sex, professional experience, etc.) were the independent variables. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, we analyzed data from 129 of the 134 healthcare professionals who completed the questionnaire, as four questionnaires were excluded because of incomplete data. In terms of gender distribution, 87.6% of the respondents were women. The median age of the participants was 40 years (IQR: 22 years). In addition, most respondents were midwives (44.2%), and the majority of them (63.6%) had a secondary-level education. The median length of professional experience was 12 years (IQR: 19 years). More than half of the participants (52.7%) were working on duty shifts (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eSociodemographic characteristics of the participants\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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e115 (89.2)\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\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e113 (87.6)\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (6.2)\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112 (86.8)\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9 (7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44 (34.1)\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\u003eHigher/University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85 (65.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidwife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51 (39.5)\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\u003eBirth attendant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (21.7)\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\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50 (38.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYears of professional experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44 (34.1)\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\u003eDay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85 (65.9)\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\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the mean Raw-TLX scores indicate a high workload across all NASA-TLX dimensions considered. The mental demand (67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7), physical demand (69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6), and temporal demand (70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5) had the highest mean scores. Performance (68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1), effort (67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6), and frustration (68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1) had slightly lower mean scores. Overall, healthcare workers\u0026rsquo; workload was moderate, with a median score of 68.4 (IQR: 6.1).\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\u003eDescriptive Statistics of Workload Dimension Scores (Raw-TLX)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkload dimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorkload level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (60.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (65.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (65.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (60.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (60.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrustration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.0 (60.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal workload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.2 (65.0\u0026ndash;72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\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\u003eThis table presents the mean values of the Raw-TLX and its six subscales. Both means and medians were reported to facilitate comparisons with the literature.\u003c/p\u003e \u003cp\u003eThe overall workload score (raw-TLX) was compared across the participants\u0026rsquo; sociodemographic characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Healthcare workers aged over 60 years had a median score of 73.7 (IQR: 2.5), whereas those aged 60 years or younger had a median score of 66.6 (IQR: 7.4); this difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.0001). Similarly, participants with more than 30 years of professional experience had a greater overall workload, with a median score of 73.3 (IQR: 2.5), than did those with 30 years of experience or less, with a median score of 66.6 (IQR: 5.8); the difference was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eWith respect to marital status, the median overall workload score ranged from 64.6 (IQR: 4.4) among single participants to 72.5 (IQR: 4.1) among widowed participants, with a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.0304). No statistically significant differences were observed for the other sociodemographic variables examined.\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\u003eComparison of overall Raw-TLX scores according to participants\u0026rsquo; sociodemographic characteristics\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall Raw-TLX Median (Q1\u0026ndash;Q3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.3 (64.2\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\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\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.8 (73.3\u0026ndash;77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.2 (65.0\u0026ndash;72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7 (64.6\u0026ndash;72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.3 (63.3\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\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\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.7 (68.3\u0026ndash;73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.8 (65.0\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.8 (68.3\u0026ndash;73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\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\u003eHigher/University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.5 (64.2\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidwife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.3 (63.3\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\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\u003eBirth attendant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.7 (68.3\u0026ndash;73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.8 (65.0\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYears of experience (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.3 (64.2\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.0 (72.5\u0026ndash;76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.9 (63.3\u0026ndash;71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\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\u003eDay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.0 (65.8\u0026ndash;73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: \u0026le;: less than or equal to; \u0026gt;: greater than; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpearman correlations between the perceived workload dimensions, the overall Raw-TLX score, age, and professional experience are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The overall workload score was significantly correlated with all six dimensions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the strongest correlations observed for frustration (ρ\u0026thinsp;=\u0026thinsp;0.73), effort (ρ\u0026thinsp;=\u0026thinsp;0.70), and performance (ρ\u0026thinsp;=\u0026thinsp;0.67). With respect to age, significant correlations were found with mental demand (p\u0026thinsp;=\u0026thinsp;0.046), physical demand (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), temporal demand (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the overall Raw-TLX score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For professional experience, significant correlations were observed with physical demand (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), temporal demand (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the overall Raw-TLX score (p\u0026thinsp;=\u0026thinsp;0.0001). No significant correlations were found with the other dimensions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlations between workload dimensions, overall score, age, and years of experience of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASA-TLX Dimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Raw-TLX ρ (p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge ρ (p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYears of experience ρ (p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 (0.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrustration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.076)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Raw-TLX score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote: TLX\u003c/b\u003e\u0026thinsp;=\u0026thinsp;task load index; ρ\u0026thinsp;=\u0026thinsp;Spearman correlation coefficient; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the multiple linear regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) indicate that, among the variables studied, only age was significantly associated with the overall workload score (B\u0026thinsp;=\u0026thinsp;0.232; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; standardized β\u0026thinsp;=\u0026thinsp;0.518).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis of factors associated with the Raw-TLX workload score\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\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnstandardized Coefficients B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% Confidence Interval for B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandardized Coefficients Beta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.00008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining specialty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: Unstandardized coefficients indicate how much the Raw-TLX score changes for a one-unit increase in the predictor variable listed in the first column.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOverall model\u003c/strong\u003e \u003cp\u003eF(6,122)\u0026thinsp;=\u0026thinsp;14.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; adjusted R\u0026sup2; = 30.5%; standard error (SE)\u0026thinsp;=\u0026thinsp;5.2%.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, workload was assessed among healthcare providers involved in delivering free maternal health services in Kananga. In accordance with the requirements set by technical and financial partners and implemented by the Ministry of Public Health, maternal health services in this context should be provided primarily by midwives, who are trained in higher education institutions focused on university-level education, scientific research, and innovation and certified after three years of training aligned with the recommendations of the International Confederation of Midwives [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOwing to the persistent shortage of qualified personnel in the DRC, other categories of providers, including nurses, are trained and retrained in ENC to support midwives in delivering maternal health services. In our study, 39.5% of the participants were midwives, 38.8% were nurses trained in ENC, and 21.7% were birth attendants retrained in ENC. This distribution reflects the structural challenges of the Congolese health system in ensuring an adequate supply of qualified personnel to provide high-quality maternal health services. However, the availability of skilled providers is a critical determinant for improving care quality and reducing maternal mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the Raw-TLX results indicated that healthcare providers on duty experienced high workloads. The overall median score was 67.5 (IQR: 8.3), corresponding to a globally high workload. These findings are consistent with reports from multiple studies among healthcare professionals, including hospital nurses, where Raw-TLX also highlighted high levels of mental workload [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This overload may be explained by the increased demand for maternal health services not accompanied by sufficient human resources, as documented in this region [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study also reported that all Raw-TLX dimensions were positively and significantly correlated with the overall score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the tool\u0026rsquo;s internal consistency in this context. The \u0026ldquo;frustration,\u0026rdquo; \u0026ldquo;effort,\u0026rdquo; and \u0026ldquo;performance\u0026rdquo; dimensions contributed the most to the overall score (correlation coefficients: 0.73, 0.70, and 0.67, respectively), suggesting that perceived overload is strongly related to psychological demands and the intensity of engagement required to perform tasks. Additionally, the age and seniority of the providers were more strongly correlated with the physical and temporal workload dimensions than with the other components.\u003c/p\u003e \u003cp\u003eThe overall workload score was significantly higher among providers aged over 60 years than among their younger colleagues (p\u0026thinsp;=\u0026thinsp;0.0001). This observation may be explained by the progressive decline in physical capacity and recovery associated with aging, which renders professional demands more challenging [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, providers with more than 30 years of experience had higher workload scores than did their less experienced colleagues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This could be attributed to their assumption of greater organizational and clinical responsibilities, increased involvement in complex decision-making, and active participation in mentoring and supervising new staff [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Higher workload levels were also observed among widowed providers; however, this association should be interpreted cautiously, as it may reflect psychosocial and personal vulnerability factors interacting with professional demands.\u003c/p\u003e \u003cp\u003eOur study further indicated that age was the only significant predictor of workload. These findings suggest that the physiological and functional changes associated with aging may play a decisive role in the perception and management of professional demands. Aging is generally accompanied by a gradual decline in certain physical capacities, including endurance, aerobic capacity, and recovery speed, after prolonged exertion. Such changes can make professional tasks more demanding for older providers, particularly in sectors with high physical and organizational requirements. A review of the work capacity of aging workers, for example, reported an average reduction of approximately 20% in physical capacity between the ages of 40 and 60 years due to musculoskeletal and cardiovascular declines, potentially affecting the ability to meet work demands and increasing perceived workload [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the healthcare sector, these effects may be particularly pronounced. Hospital activities, notably maternal health services, often involve emergencies, irregular schedules, constant vigilance, and significant decision-making responsibilities. These organizational and emotional demands can amplify the impact of age-related changes on workload perception. Several studies among healthcare professionals indicate that work capacity tends to decline with age, especially in professions with high physical demands, such as nursing or nursing aides [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Research on caregiver work capacity further shows that age is associated with decreased physical capacity and increased vulnerability to burnout when professional resources are insufficient [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the influence of age on workload may also reflect the professional roles of more experienced providers. In contexts marked by health workforce shortages, these professionals are often more heavily reliant upon due to their clinical expertise and supervisory responsibilities. This accumulation of responsibilities can increase the perceived workload independently of the physical demands of the position [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This effect may be exacerbated by task redistribution caused by personnel shortages, which intensifies the workload for existing staff, particularly the most experienced staff.\u003c/p\u003e \u003cp\u003eNevertheless, the relationship between age and workload remains complex and is highly dependent on the organizational context, working conditions, and available resources. Some studies emphasize that, despite declines in certain physical capacities, older workers may compensate through greater experience, procedural mastery, and efficiency in managing professional tasks. In well-resourced and supportive work environments, these acquired skills may help maintain work capacity despite aging [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, age emerges as an important determinant of perceived workload, but its influence results from complex interactions among individual capacity, professional experience, and the organizational characteristics of the workplace. These findings highlight the importance of incorporating demographic factors into health human resource management policies. Consideration of the workforce age structure, adaptation of working conditions, and implementation of organizational support strategies could help preserve work capacity and reduce occupational overload, particularly in services with high physical, emotional, and decision-making demands.\u003c/p\u003e \u003cp\u003eNotably, although age was the only significant factor in our model, it alone cannot explain the high workload. Perceived workload results from a complex interplay of individual, organizational, and contextual factors, such as task distribution, clinical responsibilities, staffing levels, and psychosocial conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our model may thus have been limited in capturing the full range of determinants, underscoring the need for more comprehensive analytical approaches.\u003c/p\u003e \u003cp\u003eIn the context of maternal health services, these findings are particularly important. As pillars of the maternal health system, overburdened healthcare providers face an increased risk of burnout, decreased job satisfaction, and eventual disengagement [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These factors can affect the quality and continuity of care, including reduced time spent with patients, diminished capacity to manage obstetric complications, and overall degradation of service performance.\u003c/p\u003e \u003cp\u003eIn the DRC, where maternal mortality remains high, this situation poses a major challenge to the health system and may compromise progress toward Universal Health Coverage [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Free maternal care, as an initial component of this strategy, aims to respond rapidly to health needs and save lives, but it may also increase pressure on already limited human resources [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese results underscore the need for sustainable workload management strategies. Health authorities could consider implementing gradual workforce rejuvenation, accompanied by enhanced continuous training and professional integration of new recruits. The findings also provide evidence to guide health human resource policy decisions. The Ministry of Health might adopt structured workload management strategies, including regular monitoring of staff workload. Integration of assessment tools such as the NASA-TLX or the WISN [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] into human resource planning and management processes could help anticipate overload situations and implement timely, appropriate interventions.\u003c/p\u003e\n\u003ch3\u003eStudy Limitations\u003c/h3\u003e\n\u003cp\u003eThis study had several notable limitations that may have introduced bias in the results. First, a convenience sample was used, which limits the generalizability of the findings to the broader population of healthcare professionals. Second, the study relied exclusively on self-reported data obtained via the Raw-TLX questionnaire, which may introduce social desirability bias and affect response accuracy, particularly for dimensions perceived as sensitive, such as frustration or effort. Third, although all Raw-TLX dimensions were correlated with the overall score, some correlations with age and seniority were weak or nonsignificant, suggesting that perceived workload may vary according to individual factors not measured in this study. Therefore, the results should be interpreted with caution.\u003c/p\u003e \u003cp\u003eAnother limitation concerns the interpretation of Raw-TLX scores as classified in this study. To date, the literature has not established a fixed threshold defining workload as \u0026ldquo;too low\u0026rdquo; or \u0026ldquo;too high,\u0026rdquo; despite decades of research. This lack of a clear cutoff may be due to individual differences in coping strategies, knowledge, and experience, which influence how tasks at the extremes of the workload continuum are perceived [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the multiple linear regression with robust standard errors was statistically significant, the root mean square error (RMSE) of 5.20 indicates that individual predictions may deviate on average by approximately\u0026thinsp;\u0026plusmn;\u0026thinsp;5 points from the observed scores, reflecting unexplained variability in the model. With an R\u0026sup2; of 0.305, only 30% of the variance in the total score is explained, leaving the majority of the variance potentially attributable to unmeasured or unobserved factors. Certain theoretically relevant sociodemographic variables\u0026mdash;such as sex, marital status, education level, field of study, and work schedule\u0026mdash;did not have significant effects and contributed little to predicting Raw-TLX scores, limiting the precision of estimates for individual participants. Nonetheless, the model remains useful for assessing overall trends and the relative effects of variables, with age emerging as the most influential factor.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study represents one of the first efforts to explore this domain in the context of free maternal health services. Future research could consider combining subjective and objective workload measures, as well as including larger and more diverse samples, to better understand healthcare providers\u0026rsquo; workload in this setting.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study explored the perceived workload of healthcare professionals in the context of free maternal health services. The results indicate that these professionals experience a high workload, which is associated primarily with age. These findings highlight the importance of incorporating the assessment of perceived workload into the planning and management of free maternity services. They also underscore the need to develop targeted strategies to support healthcare workers\u0026rsquo; well-being and ensure the delivery of high-quality care. Such strategies may include, for example, workforce rejuvenation and regular retraining of existing staff to better manage the current workload.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntenatal Consultation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemocratic Republic of the Congo\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Referral Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Facility\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniversal Health Coverage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Post\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Resources for Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHZ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Zone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSecondary Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASA-TLX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Aeronautics and Space Administration Task Load Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Ranges\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll ethical considerations were strictly observed in accordance with the principles outlined in the Declaration of Helsinki. The study received ethical approval from the Kinshasa School of Public Health Ethics Committee (reference no. ESP/CE/064B/2024). Additionally, verbal authorization was obtained from the administrators of the selected healthcare facilities in accordance with each institution\u0026rsquo;s internal procedures, and informed verbal consent was obtained from all participants prior to the interviews.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor details\u003c/h2\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Health Organization Management \u0026mdash; Higher Institute of Medical Techniques of Kananga, Student at the Kinshasa School of Public Health, Kananga, DRC. \u003csup\u003e2\u003c/sup\u003eFaculty of Medicine, University of Notre-Dame of Kasayi \u003csup\u003e3\u003c/sup\u003eDepartment of Health Management and Policy, Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of Congo\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. \u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon prior request.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003ePNM designed the study. PNM, DPM, JD, and ABN coordinated the data collection and study activities. EMM provided overall supervision of the study. PNM analyzed the data and drafted the initial manuscript. All the authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe researchers thank all the individuals who generously contributed to the completion of this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon prior request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInstitut National de Statistique. \u0026Eacute;cole de Sant\u0026eacute; Publique de Kinshasa. Enqu\u0026ecirc;te D\u0026eacute;mographique et de Sant\u0026eacute; (EDS-RDC III) 2023\u0026ndash;2024: Rapport final. 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Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 1727\u0026ndash;1731.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Perceived workload, Healthcare providers, Free maternity care, NASA-TLX","lastPublishedDoi":"10.21203/rs.3.rs-9075820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9075820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSince 2021, the Democratic Republic of the Congo has implemented a free maternity care policy. This policy is likely to increase the demand for maternal health services, potentially creating additional challenges in managing workload if it is not accompanied by an adequate health workforce. This study aimed to quantify the perceived workload of healthcare providers and identify associated factors within this specific policy context.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn analytical cross-sectional study was conducted among 129 healthcare professionals (midwives, birth attendants, and nurses) exhaustively recruited from 36 health facilities selected by convenience sampling. Workload was measured via the National Aeronautics and Space Administration Task Load Index (NASA-TLX) tool. Data were collected through face-to-face administration of a structured questionnaire at the end of participants\u0026rsquo; work shifts. Descriptive and inferential statistical analyses were performed via STATA version 18. Measures of central tendency, including means and medians, were calculated. The Mann\u0026ndash;Whitney and Kruskal\u0026ndash;Wallis tests were applied to compare Raw-TLX scores across groups. Spearman\u0026rsquo;s correlation was used to assess the associations between the Raw-TLX dimensions and the overall score. Multiple linear regression with robust standard errors was conducted to identify factors independently associated with Raw-TLX. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall median workload score (raw-TLX) was 69.2% (IQR 7.5), indicating a high workload. The mean scores for each workload dimension were also high: mental demand, 67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7; physical demand, 69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6; temporal demand, 70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5; performance, 68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1; effort, 67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6; and frustration, 68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1. The Spearman correlations between the Raw-TLX dimensions and the overall score were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the highest coefficients observed for frustration (ρ\u0026thinsp;=\u0026thinsp;0.73), effort (ρ\u0026thinsp;=\u0026thinsp;0.70), and performance (ρ\u0026thinsp;=\u0026thinsp;0.67). Older age (\u0026ge;\u0026thinsp;60 years) was significantly associated with increased Raw-TLX scores (B\u0026thinsp;=\u0026thinsp;0.486; p\u0026thinsp;=\u0026thinsp;0.0000).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHealthcare providers experience a globally high workload in the context of free maternity care policy, which is largely explained by advanced age. Workforce rejuvenation could help mitigate the effects of age-related workload burden. Additionally, integrating the NASA-TLX tool into health workforce management policies could enable routine monitoring of staff workload and support timely responses to excessive workload.\u003c/p\u003e","manuscriptTitle":"Perceived workload of healthcare providers delivering free maternal services: An analytical cross-sectional etudy based on the National Aeronautics and Space Administration Task Load Index (NASA-TLX) in primary health care facilities in Kananga, Democratic Republic of the Congo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:14:29","doi":"10.21203/rs.3.rs-9075820/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T16:57:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201750697839411860174251818958347384797","date":"2026-05-06T03:49:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T17:10:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160665028127869132169020489111415667525","date":"2026-03-31T16:14:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T14:38:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T09:49:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T18:27:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T17:09:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-16T14:26:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dbd26e08-ebbf-45c8-9343-80edcfb86d35","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T16:57:58+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"201750697839411860174251818958347384797","date":"2026-05-06T03:49:54+00:00","index":67,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T14:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:14:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9075820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9075820","identity":"rs-9075820","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}