Category: 3. Business

This retrospective study analyzed over 2 million COVID-19 cases from March 2020 to September 2022 across multiple COVID-19 waves, with follow-up 90 days post-diagnosis or until death. Our study underscores the significant impact of COVID-19 on PHC in the initial years of this pandemic in Catalonia. Furthermore, using machine learning models (GLMs, Lasso, Gradient Boosting, SVMs), we identified key predictors of poor outcomes, such as age, social deprivation (MEDEA), blood pressure, and a history of either diabetes, COPD, cardiovascular disease, or obesity. The models showed strong predictive accuracy (AUC: 0.73, 95%CI (0.72;0.73)–0.95, 95%CI (0.94;0.95)). Finally, using these models, an interactive web app was developed for personalized risk estimation (https://dapcat.shinyapps.io/CovidScore).

We found that the CFR was highest during the first wave of the pandemic, gradually decreasing in subsequent waves, with the second wave showing the highest incidence of cases. The higher comorbidity burden observed in the first wave may reflect limited testing availability during that period, which likely focused on more severe cases. The decrease in the CFR may be attributed to a combination of increased immunity (due to vaccination and SARS-CoV-2 infections), better identification of more severe cases, and the lower pathogenicity of recent variants like Omicron. Overall, these findings suggest changes in the SARS-CoV-2 virus, an adaptive response in healthcare, and improvements in the prevention (i.e., via vaccination) and treatment of complications as the pandemic progressed [21].

Regarding predictors of poor prognosis at the time of COVID-19 diagnosis in PHC, we identified older age, epidemic wave, social deprivation, and a history of diabetes, obesity, chronic obstructive pulmonary disease (COPD), cardiovascular disease, hypertension, and dyslipidemia. While study results vary across different populations, numerous studies have identified advanced age and comorbidities such as hypertension, cardiovascular disease, COPD, and diabetes as predictors of increased COVID-19 severity [22,23,24]. Additionally, social deprivation indicators, such as the MEDEA index, have been associated with poorer outcomes in terms of severity and mortality, underscoring the multifactorial nature of COVID-19 outcomes. Understanding and addressing these predictive factors is crucial for improving management and outcomes in affected patients. The inclusion of’epidemic wave’reflects the changing contextual factors, such as virus variants, public health measures, and healthcare strain that significantly influenced outcomes [25]. However, this limits the direct application in future waves. In such cases, the most recent wave may be used as an initial reference, while outcome data are monitored to reassess and recalibrate the model accordingly. Ideally, prediction models should be dynamically updated to remain accurate and clinically useful during future epidemic scenarios.

Conditions associated with low-grade chronic inflammation, such as obesity and diabetes mellitus, are also relevant at the metabolic level [26]. Several systematic reviews have provided consistent evidence that diabetes and obesity are associated with poorer COVID-19 outcomes, which agrees with our study [27]. Although the reasons for this association are not entirely clear, these conditions could exacerbate respiratory problems and/or affect immune responses. A systematic review and meta-analyses on high-risk phenotypes in people with diabetes determined that individuals with a more severe course of diabetes and pre-existing comorbidities had a poorer prognosis of COVID-19 than individuals with a milder course of the disease, highlighting the need for individualized and proactive management strategies for high-risk patients [28].

At the hospital level, predictive models like the ISARIC 4 C have been developed to anticipate clinical deterioration (including mortality, ICU admission, or intubation), assessing age, gender, comorbidities, and nosocomial infection [29]. A similar study in the United Kingdom, utilizing computerized PHC medical records, developed predictive algorithms for COVID-19 mortality and hospital admission risk. Factors such as age, body weight, ethnicity, and social risk explained 73% of COVID-19 deaths and 58% of hospital admissions, suggesting periodic recalibration of these models to reflect the evolving nature of the pandemic [30]. An early pandemic study on PHC identified key risk factors for ICU admission and mortality, including advanced age, male gender, autoimmune disease, bilateral pulmonary infiltrates, and elevated LDH, D-dimer, and C-reactive protein. Protective factors included myalgias, arthralgias, and anosmia [31].

Recent advances in AI and machine learning have significantly contributed to managing the COVID-19 pandemic by aiding in detection, treatment, mortality prediction, and infection modeling to reduce virus spread [32,33,34].

In this study, we used machine learning to develop predictive models and then used these models to develop an app. The app provides comprehensive information to estimate the risk of COVID-19 prognosis outcomes for individuals based on their risk factors (e.g., age, sex, comorbidities, vaccination status, COVID-19 wave). This approach could be used in PHC to identify individuals needing closer monitoring and interventions to prevent serious complications and hospitalization. Moreover, model calibration was assessed using the Brier Score, which integrates both discrimination and calibration aspects. All models showed low Brier Scores, suggesting a high level of agreement between predicted and observed risks. Given the large, representative nature of the dataset, we expect limited calibration drift within this population. Nonetheless, we acknowledge that external validation with calibration plots and slope/intercept assessment would be useful to confirm generalizability in other settings. Although COVID-19 is now endemic, this study highlights the importance of early risk stratification in primary care based on routine data at diagnosis. These findings may inform preparedness for future COVID-19 waves or other infectious outbreaks, where identifying predictors of poor outcomes at diagnosis remains key to guiding timely care.

Our study has limitations inherent to its retrospective design. Outcomes depend on the quality of existing clinical records not specifically collected for this research and have yet to undergo individual validation. Some outcomes may have been underreported or misclassified, especially if they were managed outside the hospital setting or not fully captured in the electronic health records. However, major outcomes such as mortality, hospitalization, and ICU admission are reliably recorded within the system. A notable limitation is the potential impact of vaccine implementation on epidemiology and prognosis, which underscores the need to recalibrate predictive models with post-vaccination data to maintain accuracy. Periodic updates with the latest available data are essential to ensure the continued relevance of these models. Although no predictive model is perfect for COVID-19 patients, our models serve as valuable tools to estimate the risk of complications, helping to identify patients who require closer monitoring. However, the accuracy of these models can be affected by variations in the detection and recording of symptoms and risk factors by different healthcare professionals. Moreover, mild COVID-19 cases may have gone unrecorded in PHC, potentially leading to their exclusion from our study population. Another limitation of our study is that the model was developed using subjects with confirmed COVID-19, whereas in primary care, clinicians often assess patients with suspected infection before test confirmation. This may limit applicability in early diagnostic uncertainty. Moreover, the model remains useful for risk stratification once a diagnosis is confirmed in the primary care setting. Additionally, the study benefits from using the SIDIAP database, which includes a substantial patient cohort and is a well-validated source for epidemiological and pharmaco-epidemiological studies within the Catalan primary care setting. This database not only provides standardized clinical data (including health issues, physical exams, lab results, and medication records) from pseudo-anonymized electronic health records but was also specifically updated to include COVID-19-related variables (such as diagnostic tests and procedures), enabling researchers to conduct targeted epidemiological studies. Despite the fact that we used a large, representative dataset and validated the models on a separate 25% test set, external validation was not performed. This may lead to overestimation of performance. External validation using more recent data or in other populations, such as from different regions, healthcare systems, or countries is needed to confirm the model’s generalizability and ensure robust performance of the predictive models across evolving clinical contexts.

Plant materials and treatments

Broccoli (Brassica oleracea L. var. Italica) was collected from Shulan Agricultueal Farm in Hangzhou, Zhejiang Province. ‘Naihanyouxiu’ broccoli cultivar was used as plant material in the study, which was provided from Sakata Seed Corporation. And the broccoli was harvested at commercial maturity, characterized by tightly closed florets and a deep green color. For this experiment, broccoli of the same size, consistent color, and no mechanical damage were selected.

The CLE14 peptide was synthesized by Dongheng Biomedical Co., Ltd., China. A total of 300 broccoli heads were randomly assigned to 5 groups containing 60 broccoli heads each (three replicates, 20 broccoli heads per replicate) for the experimental treatments, and sprayed with 0 (control), 10, 50, 100 or 300 µM CLE14, respectively. After being dried overnight, the treated broccoli was packaged and stored at 4 ± 0.5℃ for 28 days. The appearance attributes were evaluated every 7 days. After each evaluation, the florets from the five broccoli groups were collected for subsequent gene expression and biochemical analyses.

Broccoli yellowing index and color detection

The broccoli yellowing index (%) was used to assess the yellowing area of broccoli surfaces using the scale described by Luo et al. [22]. The color parameters of broccoli were valued using a digital colorimeter (CR-400, Konica Minolta, Japan). The values of lightness (L^*) and hue angle (H^*) were measured at five points on each broccoli every 7 days.

Measurement of nutritional quality of broccoli

The soluble sugar content of broccoli was determined using a commercial assay kit (BC0030, Solarbio, China).

The soluble protein content in broccoli was measured using a BCA protein assay kit (ml095490, Shanghai Enzyme-linked Biotechnology, China). The intracellular proteins in broccoli were extracted using PBS buffer (50 mM, pH 7.4), and the soluble protein content was calculated by measuring the absorbance at 760 nm using BSA as a standard.

Broccoli endogenous Vitamin C content was detected using the solid blue salt colorimetric method descripted by Zhang and Huang [23]. Vitamin C content was calculated by measuring the absorbance at 420 nm.

Glucosinolate levels were quantified using a glucosinolate assay kit (ml092776, Shanghai Enzyme-Linked Biotechnology). Total glucosinolate content was determined by measuring the absorbance at 505 nm.

The phenolic and flavonoid contents were measured using the Folin-Ciocalteu method and NaNO₂-Al(NO₃)₃-NaOH colorimetry method, respectively. First, broccoli samples (0.5 g) were mixed with 5 mL of methanol (80%) and subjected to ultrasonic treatment for 30 min. Supernatants were then collected for analysis. For phenolic content measurement, 200 µL of the supernatant was combined with 1 mL Folin-Ciocalteau and 800 µL Na₂CO₃ solution (75 g L⁻¹), and the mixture was incubated at 25℃ for 1 h. The absorbance was then measured at 765 nm. For the total flavonoid content test, 1 mL of the supernatant was mixed with 1 mL 70% ethanol and 0.3 mL of 5% NaNO₂. After mixing, 0.3 mL 10% Al(NO₃)₃ was added and the mixture was left to stand for 3 min at 25℃, 1 mL NaOH (1 mol L⁻¹) was then added. The reaction mixture was detected at 510 nm after 10 min using rutin as a standard.

Chlorophyll content

The chlorophyll content was assessed following the method described by Xu et al. [24]. Approximately 100 mg of broccoli powder was added to 5 mL of acetone/ethanol (2:1) solution. Chlorophyll content was analyzed based on absorbance at 664 nm and 645 nm.

RNA extraction and quantitative PCR analysis

Total RNA was extracted from broccoli florets using an RNA Simple Total RNA Kit (Tiangen, China) with DNase I treatment. A ReverTra Ace quantitative (qPCR) reverse transcription (RT) kit (Toyobo, Japan) was used to synthesize cDNA. qRT-PCR assays were performed using the StepOne detection system (Thermo Fisher Scientific, USA) and SYBR Green PCR Master Mix Kit (Takara, Japan). Gene expression levels were calculated using the comparative ΔΔCt method. Briefly, the average Ct value for the target gene was normalized to the average Ct value of the endogenous reference gene (BoActin) to obtain the ΔCt value for each sample: ΔCt = Ct (target gene) – Ct (reference gene). The ΔΔCt value was then calculated as: ΔΔCt = ΔCt (test sample) – ΔCt (untreated sample). The relative fold change in gene expression was determined as 2^(-ΔΔCt). Three independent biological replicates were analyzed per experimental group. Sequences of primers are listed in Table S1.

RNA-seq analysis

Three independent repeats from the control and CLE14-treated broccoli at 0, 7, 14, 21, and 28 d post-harvest were used for RNA-seq analysis. RNA was extracted using the RNAprep Pure Plant Kit (Tiangen, China) and quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). RNA integrity was determined using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system. Biomarker Technologies (Beijing, China) prepared Illumina libraries and sequenced them on an Illumina NovaSeq 6000 sequence platform (150 bases paired-end reads; Illumina, USA). The raw reads were further processed using the bioinformatics pipeline tool, and the BMKCloud (www.biocloud.net) online platform. Raw reads in fastq format were first processed through in-house Perl scripts, and the reads containing adapter or poly-N and low-quality reads were removed to obtain clean reads for further analyses. The proportion of clean reads in the samples (Q30) ranged from 94.62 to 97.82%. The cleaned reads were aligned to the Brassica oleracea reference genome from NCBI using hisat2 tools soft with default parameters. Gene expression levels were quantified by mapping fragments per kilobase of transcript per million fragments mapped (FPKM). Additional, differential expression analysis between treatments were performed using the DESeq2 package, and the genes with an adjusted P-value < 0.01 & Fold Change ≥ 2 were assigned as differentially expressed. Randomly chosen deferentially expressed genes were verified using qRT-PCR. The weighted gene co-expression network analysis (WGCNA) (min Module Size 30, and merge Cut Height, 0.25) was used to build a co-expression network. Data were analyzed using the online BMKCloud bioinformatics platform.

Malondialdehyde (MDA), H₂O₂ content and O₂
^•− production rate

The MDA levels were measured following Xu et al. [24]. Broccoli floret powder was mixed with 5 mL trichloroacetic acid, and the supernatant was collected via centrifugation. The supernatant was combined with 2 mL of thiobarbituric acid (0.67%) and boiled for 20 min. The MDA content was analyzed by measuring the absorbance at 450, 532, and 600 nm.

H₂O₂ content was measured using a Micro Hydrogen Peroxide (H₂O₂) Assay Kit (BC3590, Solarbio, China).

The generation rate of superoxide anions (O₂^•−) was determined using hydroxylamine oxidization according to Shi et al. [25]. Briefly, broccoli (0.5 g) was mixed with 2 mL of PBS buffer, and the supernatant was collected. The supernatant was mixed with 100 µL NH₂OH·HCl (1 mM), 1 mL PBS buffer and subjected to incubation at 25℃ for 1 h. Following this, 1 mL C₆H₇NO₃S (17 mM) and 1 mL C₁₀H₇NH₂ (7 mM) were added. The generation rate of O₂^•− was calculated based on absorbance at 530 nm after incubation at 25℃ for 20 min, using NaNO₂ as the standard.

Enzyme activities

The activities of pheophorbide a oxygenase (PAO) and pheophytinase (PPH) were measured using Plant enzyme activity Kits (Enzyme-linked Biotechnology).

Catalase (CAT) and superoxide dismutase (SOD) activity was measured as described by Cheng et al. [26]. Broccoli floret powder (0.5 g) was added to 3 mL of PBS buffer (50 mM, pH 7.8, with 0.2 mM EDTA, 2mM AsA, 25 mM HEPES, and 2% polyvinylpolypyrrolidone [w/v]). The supernatant was then collected for enzyme activity assay, which was performed using a SHIMADZU UV-2600 spectrophotometer (Shimadzu, Kyoto, Japan).

Statistical analysis

All data were analyzed using Statistical Analysis System version 8 (SAS Institute Inc., Cary, NC, USA). The significance of treatment differences was analyzed using ANOVA followed by Tukey’s test at the 5% significance level. Data were recorded as the mean ± standard errors (SE) of at least three independent biological replicates.

Digitalization is transforming electoral processes across the European Union and its aspiring members. While it strengthens democratic participation, it also introduces risks, from opaque political financing and disinformation to foreign interference and cybersecurity threats. These challenges demand strong digital governance to keep elections free, fair and transparent within and beyond EU borders. 

The EU’s digital acquis is central to this effort, shaping how elections are conducted both in Member States and candidate countries. These countries, often with limited resources, must align with the acquis while confronting issues such as foreign influence and effective online campaign oversight. Their experiences also offer valuable lessons for the EU.

This research, Navigating the European Union’s Digital Regulatory Framework, developed under the project Closing the Digital Gap on Elections in EU Accession, funded by Stiftung Mercator, addresses a critical gap in the interaction between the EU and candidate and potential candidate countries.

Part 1, A Compact Overview of Its Impact on Electoral Processes, examines the EU’s digital rulebook—anchored in the Artificial Intelligence Act, the Digital Services Act, the European Media Freedom Act, the General Data Protection Regulation and the Regulation on the Transparency and Targeting of Political Advertising. It shows how these frameworks align technology with democratic values and guard against cyberthreats, privacy breaches and opaque online campaigning.

Part 2, Perspectives on Electoral Processes in EU Candidate Countries, analyses progress in aligning with the EU acquis across Albania, Moldova, North Macedonia and Ukraine. Based on in-house and field research, it assesses legal, institutional and enforcement capacities to counter digital threats to elections.
The findings offer comprehensive yet concise guidance for electoral bodies, policymakers and civil society as well as EU institutions. They also lay the groundwork for stronger cooperation and knowledge exchange. 

With candidate countries aiming to complete EU-related reforms by 2030, and the EU reinforcing digital safeguards through initiatives like the European Democracy Shield, this research contributes to protecting democratic processes and deepening EU–candidate country ties.

Policy Priorities and Recommendations

• Build institutional and societal resilience by strengthening coordination among EU institutions, national authorities and electoral management bodies (EMBs) in the EU and candidate countries, ensuring clear mandates and promoting the exchange of good practices, enhancing digital literacy and preparedness throughout the electoral cycle.
• Safeguard fundamental rights and democratic values in elections by regulating data use, AI and online political activity to prevent manipulation, disinformation and privacy breaches.
• EU candidate countries should prioritize alignment with the EU digital acquis to strengthen electoral integrity and democratic resilience.

For more information visit the project page: Closing the Digital Gap on Elections in EU Accession
 

Broadly framed concerns

The CNMC focused on two markets linked to cancer detection tests where the parties’ activities overlap:

• The supply of PSMA PET radiopharmaceuticals where the CNMC found the parties have combined market shares of over 80%
• The provision of PET radiopharmaceuticals contract manufacturing services (CMO) to third parties in north-eastern Spain (Operators without their own infrastructure (cyclotron) depend on these services to supply PET radiopharmaceuticals and, post-merger, the CNMC says their supply options would be reduced from three to two.)

The authority is concerned that the transaction will result in higher prices and reduced product variety, as well as increased barriers to entry and expansion, and possible foreclosure of rivals.

Significantly, the CNMC also alleged that the merger may lead to coordinated effects between the two remaining cyclotron operators in north-eastern Spain, meaning they may be more likely to coordinate their conduct and harm effective competition.

Behavioral remedies were not enough

Curium offered “a series of commitments” to address the CNMC’s concerns. These were all behavioral commitments, including to:

• refrain from manufacturing or marketing its PSMA from IRAB’s Barcelona cyclotron facility until other PSMA radiopharmaceuticals are effectively commercialized in north-eastern Spain
• continue marketing IRAB’s PSMA under the same conditions for a minimum period of time
• increase certain manufacturing capacity at IRAB’s facility
• offer any new CMO contracts with third parties at a standard level of service and on market terms
• improve the production capacity of IRAB’s cyclotron.

However, the CNMC considered that the commitments could not address its horizontal and vertical concerns, in part because of their “very limited duration.” According to the authority, the proposed commitments also failed to alleviate the risk of coordinated effects between the two remaining cyclotron operators in north-eastern Spain. Those operators had already been fined, along with two executives, a total of EUR5.76 million for adopting a joint strategy to share contracts for the supply of radiopharmaceuticals to public and private hospitals.

The CNMC ultimately concluded that no other conditions, short of blocking the deal, could resolve the alleged structural risks.

The prohibition—the CNMC’s first—is an outlier and may reflect the sensitive nature of the markets involved. Previous mergers raising CNMC antitrust concerns have tended to be resolved with remedies. And, in the majority of these cases, the remedies have comprised behavioral commitments (either on their own or combined with structural divestments), showing the CNMC’s general openness to such remedies, despite their reluctance to accept them in this case.

The CNMC’s decision is not, however, final. In the case of a phase 2 prohibition (among other scenarios), Spanish law allows for a “phase 3” review. This means that the CNMC will refer the case to the Minister of Economy, Trade and Enterprise, which will then decide whether to forward the merger to the Council of Ministers to assess if any public interest criteria outweigh antitrust considerations.

Three key takeaways

1. Expect deals with an impact on consumer health to be closely scrutinized.

Transactions in sectors that have a direct impact on consumer welfare or health—particularly those involving innovative technologies or products—will be assessed carefully by antitrust authorities.

The CNMC’s close scrutiny of markets related to cancer detection tests strikes certain parallels with the European Commission’s (EC) 2022 assessment of Illumina’s acquisition of cancer detection test developer GRAIL.

There, too, the EC blocked the merger after rejecting the parties’ offer of behavioral commitments (although ultimately the prohibition was overturned for lack of jurisdiction). The authority noted the importance of preserving competition between early cancer detection test developers and of enabling consumers to access technology at competitive prices and with a choice of suppliers.

2. Be aware that prior anticompetitive conduct may exacerbate concerns with a deal.

The CNMC’s case is not the only merger assessment where we have seen an antitrust authority refer to previous cartel conduct when articulating its concerns over the possible impact of a transaction.

Parties should expect consolidation in any sectors that have seen recent antitrust enforcement to attract special scrutiny, particularly where markets are (relatively) concentrated.

3. Explore early whether any antitrust concerns can be remedied.

The 2025 merger control enforcement landscape is shaping up to be rather different than previous years. Rising deal mortality levels are being replaced by a more permissive merger control environment, as many antitrust authorities face political pressure to support economic growth and take a pro-business approach.

However, this Spanish case is a reminder that sometimes an antitrust authority views the markets involved as too sensitive, or the antitrust concerns too complex, for a merger to be waved through.

Where antitrust hurdles are expected, early strategic planning and close engagement with authorities during pre-notification is advisable. This will enable parties, at the initial stages of a merger assessment, to test the nature of an authority’s concerns and to discuss the feasibility and acceptability of possible remedies.

Any remedy offer put forward by the merging parties should be carefully designed and robust, especially if it contains behavioral elements.

Agreement covers 41 APS5000 auxiliary power units on combined fleet of Boeing 787 Dreamliner aircraft

LONDON, Oct. 14, 2025 /PRNewswire/ — Pratt & Whitney Canada signed a 14-year maintenance and support agreement with Lufthansa Airlines and Austrian Airlines, both subsidiaries of the Lufthansa Group. The agreement covers the 41 APS5000 auxiliary power units (APUs) on the two airlines’ combined fleet of Boeing 787 aircraft. Pratt & Whitney is an RTX (NYSE: RTX) business.

“Designed for the Boeing 787 Dreamliner, our APS5000 APU provides power to the aircraft when the main engines are shut down and ground power is unavailable,” says Anthony Rossi, vice president, Customer Service, Pratt & Whitney Canada. “Additionally, the APS5000 simultaneously powers twin electric starters for the main engines making it vital to overall dispatch reliability. The maintenance plan we have developed for the two airlines of the Lufthansa Group is flexible and ensures predictable costs while maximizing performance and time between maintenance.”

“The growing Boeing 787 fleet is set to become a backbone of our long-haul operations, and with that comes the need to prioritize reliability, commercial efficiency, and innovation in every aspect of our technical operations,” said Binoj Sebastian, senior director, Technical Procurement, Lufthansa Airlines. “This long-term maintenance agreement with Pratt & Whitney Canada underscores our confidence in the APS5000 APU and its original equipment manufacturer as the best partner to deliver consistent value to our daily operations. Their proven expertise and product performance will be instrumental in supporting the availability and efficiency of our Dreamliner fleet.”

The APS5000 is the quietest APU in its class with the lowest emissions in the industry. It produces 450kVA of electrical power at sea level and starts and operates up to 43,100 feet. More than 1,400 APS5000 APUs have been manufactured, and the fleet has flown nearly 16 million hours. Pratt & Whitney Canada’s maintenance programs for its APU fleet deliver flexibility and predictable costs while maximizing performance and long maintenance intervals.

About Pratt & Whitney
Pratt & Whitney, an RTX business, is a world leader in the design, manufacture and service of aircraft engines and auxiliary power units for military, commercial and civil aviation customers. Since 1925, our engineers have pioneered the development of revolutionary aircraft propulsion technologies, and today we support more than 90,000 in-service engines through our global network of maintenance, repair and overhaul facilities.

About RTX
RTX is the world’s largest aerospace and defense company. With more than 185,000 global employees, we push the limits of technology and science to redefine how we connect and protect our world. Through industry-leading businesses – Collins Aerospace, Pratt & Whitney, and Raytheon – we are advancing aviation, engineering integrated defense systems for operational success, and developing next-generation technology solutions and manufacturing to help global customers address their most critical challenges. The company, with 2024 sales of more than $80 billion, is headquartered in Arlington, Virginia.

For questions or to schedule an interview, please contact [email protected]

SOURCE RTX

Introduction

Healthcare systems are distinguished by the urgency of care delivery and the complexity of patient needs, making the performance of frontline providers, especially nurses, pivotal to healthcare outcomes.¹ Among healthcare professionals, nurses constitute the largest workforce and maintain the closest interaction with patients, directly influencing the quality of care and patient safety.² Critical care nurses, in particular, are instrumental in improving clinical outcomes, reducing morbidity and mortality, minimizing complications and medical errors, and optimizing healthcare costs.^3,4 As such, their job performance is essential not only to individual patient care but also to the overall effectiveness and efficiency of healthcare organizations.⁵

Job satisfaction among nurses has been identified as a foundational determinant of job performance.^6,7 Defined as the affective orientation an individual hold toward their work environment, job satisfaction encompasses attitudes toward tasks, et al, supervisors, and institutional policies.^8,9 Higher job satisfaction is consistently associated with improved job performance, patient safety, and workforce retention, whereas dissatisfaction contributes to high turnover and diminished care quality.^3,10,11

Equally critical is the concept of work engagement, defined as “a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption”. These sub-factors represent one’s internal drive to reach specific goals. Vigor refers to “high levels of energy and mental resilience while working and the willingness and ability to invest effort in one’s work”.¹² Nurses with high engagement levels tend to demonstrate enthusiasm, resilience, and sustained involvement in their roles, which in turn fosters productivity and job satisfaction.¹³ Recent literature suggests that work engagement may act as a motivational mechanism linking job satisfaction with performance outcomes, particularly in high-stress environments like critical care units.^13,14

These constructs (job satisfaction, work engagement, and job performance) are central to organizational psychology and nursing science. The Job Demands-Resources (JD-R) model offers a theoretical lens to understand their interrelationships.¹⁵ According to Bakker and Demerouti (2018), job resources (eg, autonomy, support, feedback) foster engagement, while job demands (eg, workload, emotional exhaustion) can lead to burnout. When nurses perceive sufficient job resources, they are more likely to exhibit vigor, dedication, and absorption, which are the key indicators of engagement.¹³ Additionally, the Social Exchange Theory (SET) explains the reciprocal relationship between job satisfaction and performance. SET posits that workplace relationships are built on a norm of reciprocity: when employees perceive fair treatment, support, and trust from their organization, they feel morally obligated to return the favor through positive attitudes and behaviors, such as improved performance, commitment, and reduced turnover. In nursing practice, this may manifest as greater patient advocacy, teamwork, and a willingness to go beyond minimum duties when nurses feel valued and supported by their supervisors and institutions. This theoretical framework underscores why emotionally and psychologically satisfied nurses are more likely to remain engaged and contribute meaningfully to organizational goals. Both frameworks support the proposition that work engagement may serve as a motivational bridge or moderator between satisfaction and performance, particularly in complex, high-stress environments like critical care units. Employees satisfied with their work environment are more likely to reciprocate with higher performance and organizational loyalty.¹⁶ These psychological frameworks support the proposition that work engagement may act as a mediator or moderator in the satisfaction–performance relationship.

Substantial global evidence affirms the link between job satisfaction, work engagement and nurse performance, particularly in high stress health care environments. Studies across varied context, including China and Malaysia, report that satisfied and engaged nurse deliver higher-quality care and exhibit lower turnover intentions.^2,12 While regional research in the Middle East is emerging, many of the challenges such as administrative burden, limited autonomy and structural stressors^17,18 mirror those in Jordan.

Importantly, recent literature positions work engagement not only as an outcome of job satisfaction but also as a potential moderator. That is, even when job satisfaction is high, performance may vary depending on engagement levels. According to Khusanova, Kang and Choi,¹⁹ engagement amplifies employees’ willingness to invest their physical, emotional, and cognitive resources, thereby enhancing job performance. Engaged nurses report higher resilience, stronger interpersonal relationships, and better task focus—all of which contribute to more effective clinical care.²⁰ Despite this theoretical support, empirical evidence for this moderating role is still limited, particularly within nursing populations. Specifically in Jordan, critical care nurses confront high patient acuity, emotional exhaustion and staffing shortages.,²¹ Al-Hamdan, Manojlovich and Tanima²² documented a nurse turnover rates exceeding 36.6%,²² attributing it dissatisfaction with pay, poor career progression, and unsustainable workloads. These factors compromise not only engagement and morale but also threaten care quality and safety.²³ These factors that also diminish engagement and morale. This issue is particularly pressing in intensive and critical care units, where nurses face demanding patient loads, emotional stress, and rapid clinical decision-making. These environments demand not only technical proficiency but also sustained engagement and psychological resilience.^10,24 Jordanian critical care nurses face unique stressors such as emotional exhaustion, high patient acuity, and inadequate staffing, which collectively compromise job satisfaction and engagement.¹⁰ These occupational and systemic stressors contribute to a cycle of disengagement, dissatisfaction, and diminished performance.

Yet, little is known about how psychological constructs like job satisfaction and engagement influence nurse performance in these settings. Despite emerging regional research, the specific pathways linking job satisfaction and performance, particularly through engagement, remain underexplored in Arab countries. There is a notable lack of empirical studies examining these dynamics in critical care settings, where performance expectations and environmental stressors are uniquely high. Furthermore, no known studies in Jordan have examined how work engagement moderates the relationship between job satisfaction and job performance among critical care nurses. This oversight limits the development of evidence-based strategies aimed at enhancing nurse retention, improving job performance, and ultimately ensuring high-quality patient care.

This study seeks to address this critical gap by:

• Assessing the direct relationships between job satisfaction, work engagement, and job performance;
• Examining the moderating effect of work engagement on the relationship between job satisfaction and job performance; and
• Exploring contextual and demographic variables that may influence these constructs.

Specifically, this research focuses on nurses working in critical care units in Jordan, aiming to clarify how work engagement shapes the satisfaction–performance link.

Methods

Study Design

This study employed a descriptive cross-sectional correlational design to explore the relationships among job satisfaction, work engagement, and job performance among registered nurses working in critical care units in Jordan. The design allowed the researcher to examine multiple variables simultaneously and to assess associations among them without manipulating any variables.²⁵

Study Setting

The study was conducted in four major governmental hospitals in central Jordan, under the jurisdiction of the Ministry of Health (MOH). These hospitals provide a significant portion of Jordan’s public healthcare services: Al-Basheer Hospital in Amman, the largest public hospital in Jordan, includes four hospital wings and approximately 1,000 beds staffed by over 1,300 nurses; New Zarqa Governmental Hospital, with 464 beds and 369 nurses; Al-Salt Governmental Hospital, with 450 beds and 432 nurses; and Prince Faisal Hospital, with 169 beds and 174 nurses. These hospitals were chosen due to their size, diversity of critical care services, and representation of the general public healthcare context in Jordan.

Study Population and Sampling

The target population comprised all registered nurses working in critical care units across the selected governmental hospitals. A convenience sampling technique was used, with inclusion criteria as follows: Registered Jordanian nurses; Currently employed in a critical care unit; Having a minimum of one year of work experience at the current hospital; Willingness to participate voluntarily in the study.

The sample size was estimated using G*Power version 3.1.9.2.²⁶ Based on a significance level of α = 0.05, power = 0.95, and a medium effect size of 0.15 for linear multiple regression with two predictors, the minimum required sample size was calculated to be 107. An additional 30% was recruited to account for non-response, resulting in a final target sample of 140 participants.

Instruments

Data were collected using a self-administered online questionnaire, composed of the following sections:

1. Demographic and Professional Characteristics Sheet: Developed by the researcher, this section gathered data on age, gender, marital status, education, and years of professional and unit-specific experience.
2. Utrecht Work Engagement Scale (UWES-9): It measured work engagement;²⁷ which assesses three dimensions: vigor, dedication, and absorption. Each subscale contains three items rated on a 7-point Likert scale (0 = Never to 6 = Always). The scale has shown strong psychometric properties; Cronbach’s alpha = 0.92, with subscale reliabilities ranging from 0.76 to 0.89.^27–29 Higher scores indicating higher engagement and lower scores indicating lower engagement, cut-off values were statistically specified to give a clear meaning for the calculated scores for high (4–6), moderate (2 < x < 4), and low (0 < x < 2) reported engagements^30,31).
3. Six Dimension Scale of Nursing Performance (SDSNP): It used to assess the Job Performance. SDSNP is developed by Schwirian,³² which evaluates leadership, critical care, teaching/collaboration, planning/evaluation, interpersonal communication, and professional development across 52 items. Each item is rated on a 4-point Likert scale. Internal consistency for the overall scale is high (α = 0.95), with subscale reliabilities ranging from 0.65 to 0.85.³³
4. Job Satisfaction Survey (JSS): It used to evaluate the Job Satisfaction. JSS is developed by Spector³⁴ was used to assess nine core dimensions of job satisfaction: pay, promotion, supervision, benefits, contingent rewards, operating procedures, et al, nature of work, and communication. In this study, we used the short version of the JSS, comprising seven items, with permission from the original authors of two prior studies (Kamal et al, 2012; Dargahi & Shaham, 2011) who had adapted it for healthcare contexts.^35,36 Each item was rated on a 4-point Likert-type scale with the following anchors: 4 = Very Dissatisfied, 3 = Dissatisfied, 2 = Satisfied, 1 = Very Satisfied. It is important to note that the original JSS does not prescribe formal cutoff scores or published mean norms to determine satisfaction levels. Therefore, to support interpretation, we adopted approximate cutoff ranges used in previous regional studies, purely for analytical clarity.³⁷ Higher mean scores indicate greater dissatisfaction, and lower mean scores reflect higher satisfaction. For this study, we report descriptive means rather than classifying levels of satisfaction categorically.^36,38

In this study, we utilized the English version of the questionnaires. A pilot study was conducted with 40 nurses outside the primary sample to assess the validity and reliability of the instruments. The results demonstrated a Cronbach’s alpha of 0.87, indicating good internal consistency. Face validity was confirmed by participating nurses, while content validity was established through expert review, with consensus on the appropriateness of all items. Construct validity was supported by correlation coefficients ranging from 0.69 to 0.89. Furthermore, the Kaiser-Meyer-Olkin (KMO) measure was 0.89, and Bartlett’s test of sphericity was statistically significant, both confirming the suitability of the data for factor analysis and supporting the overall validity of the scale. The expected time to complete the online questionnaires was 15 minutes.

Ethical Considerations

Ethical approval for this study was obtained from the Institutional Review Board (IRB) of the primary investigator’s university (Approval No: 25/2023), followed by formal permissions from the Jordan Ministry of Health and the respective hospital administrations. Participants’ rights to anonymity and confidentiality were upheld through multiple safeguards: (1) a clear explanation of the study’s purpose and significance, (2) the use of a participant consent form, (3) coded, sealed questionnaires, (4) assurance of the right to voluntarily withdraw at any point, and (5) a guarantee that only aggregated results would be shared. To address ethical concerns regarding voluntary participation, a consent form, study objectives, and confidentiality assurances were included with the questionnaire package. Participation was entirely voluntary and free from coercion. All data, including participants’ WhatsApp numbers, were securely stored on the principal investigator’s password-protected personal computer, accessible only by the researcher. Additionally, the researcher’s WhatsApp number was provided to address any participant inquiries.

Data Collection Procedure

Data collection commenced following the receipt of IRB approval. The primary investigator (PI) visited the nurse managers at each hospital and explained the study’s objectives and significance. The PI then proceeded to the designated data collection units—including the Intensive Care Units (ICU), Cardiac Care Units (CCU), and Neonatal Intensive Care Units (NICU) to introduce herself to the responsible nurse managers and further clarify the study procedures.

After obtaining verbal approval and agreement from the nursing staff to participate, the nurses suggested distributing the study materials through their WhatsApp workgroups. Contact information for the unit managers was collected to facilitate communication with participants. An electronic, web-based questionnaire package was created using online software. This package included the informed consent form, study objectives, instructions for completing the questionnaires, and the study instruments. The invitation link was first sent to the unit managers via WhatsApp, who then shared it with their respective WhatsApp workgroups. A reminder message was sent by the managers to all participants one week after the initial invitation to encourage participation. Data collection was conducted from July 14, 2024, to August 4, 2024. A total of 210 eligible nurses were invited to participate. Of these, 143 completed the survey, yielding a response rate of 68.1%. The minimum required sample size was 140, which was successfully exceeded.

Data Analysis

The data were checked for completeness and accuracy. Missing or invalid responses were excluded, and outliers were identified and removed using statistical methods (such as box plots). Coding and data entry were done before starting the data analysis process. Then, the assumptions for using parametric statistical tests were checked before starting the process (each individual in the sample is independent of others, normally distributed, homogeneity of variance). IBM SPSS version 26 was used to analyze the data (SPSS 26). Descriptive statistics (means, standard deviation, and frequencies) were used to present the characteristics of Jordanian registered nurses who participated in the study.

An independent-sample t-test was used to determine whether significant differences existed in job satisfaction and performance levels based on marital status, educational attainment, shift pattern, and gender. Pearson’s r was used to test the relationship between continuous demographics and job satisfaction and job performance. Also, Pearson’s correlation was used to test the relationship between job satisfaction and job performance, the relationship between job performance and work engagement, and the relationship between job satisfaction and work engagement. Moderation analysis was performed (using regression test) to determine the moderating role of work engagement in the relationship between job satisfaction and job performance.

Results

Sociodemographic and Professional Characteristics

A total of 143 nurses participated in the study. The mean age of participants was 28.7 years old (SD = 7.1 years). More than half of the participants were females (n = 80, 55.9%). In terms of marital status, the majority of the participants were unmarried (n = 92, 64.3%). When asked about their educational attainment, most participants responded that they had completed an undergraduate degree (ie, Bachelor of Science) (n = 124, 86.7%). The mean length of practicing as a nurse was six years (SD = 6.4 years), while the mean length of working in their current hospital department was 3.6 years (SD = 3.9 years). In terms of shift pattern, more than half of the participants worked on a rotating shift (n = 78, 54.5%). There is a wide range in the number of patients cared for by participants per shift, with an average number of 6 patients (SD = 12 patients). Lastly, the average monthly income was 694.1 JD (SD = 1,104.9 JD). Table 1 shows the sociodemographic and professional characteristics of participants.

Table 1 Sociodemographic and Professional Characteristics (n = 143)

The Level of Job Satisfaction, Work Engagement and Job Performance

Results showed that participants had a moderate level of job satisfaction (m = 2.90 out of 4, SD = 0.35) (Table 2). The item with the highest score was item 5 (“I feel proud when I tell others that I am an important part of hospital staff”) (m = 3.21 out of 4, SD = 0.59). On the other hand, the item with the lowest score was item 6 (“I feel satisfied about my salary”.) (m= 2.45 out of 4, SD = 0.85).

Table 2 The Descriptive Statistics of Job Satisfaction, Work Engagement, and Job Performance (n=143)

Results also showed that participants had moderate levels of work engagement (m = 3.58 out of 6, SD = 0.95) (Table 2). The item with the highest score was item 7 (“I’m proud of the work that I do”.) (m = 4.18 out of 6, SD = 1.47) while the item with the lowest score was item 5 (“When I get up in the morning, I feel like going to work”.) (m = 3.21 out of 6, SD = 1.25).

Two characteristics of job performance were measured in the study. One is the character of frequency (Table 2) (ie, how often a particular skill is performed), and the other is quality (Table 2) (ie, how satisfactory the level of performance on a particular skill). Results showed that nurses frequently performed (m = 3.31 out of 4, SD = 0.56) and performed well on specific nursing tasks (m = 3.11 out of 4, SD = 0.56). On the other hand, the dimensions of job performance were measured for the nurse participants. The dimension with the highest score is Critical Care (m = 3.16 out of 4, SD = 0.61), while the dimension with the lowest score is Teaching (m = 3.06 out of 4, SD = 0.59) (See Table 2).

In terms of the frequency of nursing performance, the three tasks or skills that were most frequently performed were (1) item 21 (“Promote the patient’s rights to privacy”.) (m = 3.59 out of 4, SD = 0.62), followed by (2) item 24 (“Explain nursing procedures to a patient prior to performing them”.) (m = 3.50 out of 4, SD = 0.76), and (3) item 27 (“Perform appropriate measures in emergency situations”.) (m = 3.50 out of 4, SD = 0.69). On the other hand, the least frequently performed tasks or skills were (1) item 5 (“Identify and use community resources in developing a plan of care for a patient and his/her family”.) (m = 3.07 out of 4, SD = 0.84), followed by item 3 (“Give praise and recognition for achievement to those under his/her direction”.) (m = 3.08 out of 4, SD = 0.83), item 10 (“Initiate planning and evaluation of nursing care with others”.) (m = 3.15 out of 4, SD = 0.83), item 14 (“Develop innovative methods and materials for teaching patients”.) (m = 3.15 out of 4, SD = 0.90), and item 17 (“Help a patient communicate with others”.) (m = 3.15 out of 4, SD = 0.87).

In terms of the quality of nursing performance, the items that were very well performed were item 11 (“Perform technical procedures such as oral suctioning, tracheostomy care, IV therapy, catheter care, and dressing changes”.) (m = 3.26 out of 4, SD = 0.78), followed by item 47 (“Maintain high standards of performance”.) (m = 3.24 out of 4, SD = 0.79), and item 13 (“Identify and include immediate patient needs in the plan of nursing care”.) (m = 3.23 out of 4, SD = 0.77). On the other hand, tasks or skills that were not performed well were item 5 (“Identify and use community resources in developing a plan of care for a patient and his/her family”.) (m = 2.93 out of 4, SD = 0.85), followed by item 31 (“Encourage the family to participate in the care of the patient”.) (m = 3.01 out of 4, SD = 0.87), item 32 (“Identify and use resources within the health care agency in developing a plan of care for a patient and his/her family”.) (m = 3.01 out of 4, SD = 0.82), item 17 (“Help a patient communicate with others”.) (m = 3.03 out of 4, SD = 0.78), item 19 (“Give emotional support to a family of dying patient”.) (m = 3.03 out of 4, SD = 0.87), item 29 (“Use teaching aids and resource materials in teaching patients and their families”.) (m = 3.03 out of 4, SD = 0.78), item 38 (“Communicate facts, ideas, and professional opinions in writing to patients and their families”.) (m = 3.03 out of 4, SD = 0.79), and item 43 (“Use learning opportunities for ongoing personal and professional growth”.) (m = 3.03 out of 4, SD = 0.80) (See Table 2).

The Relationship Between Main Variables; Job Satisfaction, Work Engagement and Job Performance

Table 3 shows the relationship between job satisfaction and work engagement; the results showed that job satisfaction has a significant, positive, but weak relationship with work engagement (r = 0.28, p < 0.001). Participants with high levels of work engagement had high levels of job satisfaction. Job performance frequency (r = 0.48, p < 0.001) and quality (r = 0.49, p < 0.001) had a significant, positive, and moderately strong relationship with work engagement. Participants with high levels of work engagement had high levels of frequency and quality of job performance. Results showed that job satisfaction had a significant, positive, and weak relationship with quality of job performance (r = 0.21, p < 0.05) but not with its frequency (r = 0.107, p = 0.202). Participants who had high levels of job satisfaction also had high levels of job performance quality (See Table 3).

Table 3 The Relationship Between Main Study’s Variables

The Job Satisfaction and Job Performance Based on Sociodemographic Characteristics

Independent t-test was used to determine whether significant differences existed in job satisfaction and job performance levels based on marital status, educational attainment, shift pattern, and gender. No significant differences were found in job performance scores based on the demographic variables (Table 4). However, female participants had significantly higher mean scores on job satisfaction (M=2.96, SD= 0.33) than their male counterparts (M= 2.83, SD=0.36) (P= 0.03) (See Table 4).

Table 4 The Job Satisfaction and Job Performance Based on Sociodemographic Characteristics

Pearson’s r was used to test the relationship between continuous demographics and job satisfaction and performance. Results showed that the quality of job performance has a significant negative relationship with patient load (r = −0177, p < 0.05). In contrast, the frequency of job performance has a significant negative relationship with patient load (r = −0.206, p < 0.05) and positive relationship with experience in the current department (r = 0.17, p < 0.05). Job satisfaction has a significant positive relationship with income (r = 0.180, p < 0.05) (See Table 4).

Moderating the Role of Work Engagement on the Relationship Between Job Satisfaction and Job Performance

Moderation analysis was performed to determine the moderating role of work engagement in the relationship between job satisfaction and job performance (Tables 5 and 6). The model explained 28% of the frequency of job performance and 24% of its quality. Results showed that work engagement was a significant moderator in the relationship between job satisfaction and the frequency of job performance (See Table 5 but not in the relationship between job satisfaction and the quality of job performance (See Table 6).

Table 5 Moderating the Role of Work Engagement on the Relationship Between Job Satisfaction and Frequency of Job Performance

Table 6 Moderating the Role of Work Engagement on the Relationship Between Job Satisfaction and Quality of Job Performance

As noted in the Figure 1, the impact of job satisfaction on job performance frequency was positive among participants with high work engagement but unfavorable among people with low work engagement, participants with high levels of work engagement had high levels of job satisfaction (See Figure 1).

Figure 1 The impact of job satisfaction on frequency of job performance with moderation of work engagement.

Discussion

The findings of this study revealed that nurses reported moderate levels of job satisfaction (M = 2.90), work engagement (M = 3.58), and job performance in both frequency (M = 3.31) and quality (M = 3.11). Work engagement showed a moderate, significant positive correlation with both the frequency and quality of job performance, and a weak but significant correlation with job satisfaction. Job satisfaction was weakly but significantly associated with job performance quality, but not with its frequency. Moderation analysis confirmed that work engagement significantly moderated the relationship between job satisfaction and job performance frequency, though it did not moderate the satisfaction–quality performance link. This discrepancy may be explained by task complexity and measurement limitations. Frequency reflects consistent task execution, which engagement may enhance through increased energy and focus. In contrast, quality may be influenced by contextual factors such as clinical competence, autonomy, and feedback, which were not directly captured in this study’s variables. Hence, engagement alone may not be sufficient to affect perceived quality of performance. Additionally, sociodemographic factors such as gender, income, and patient load were found to influence job satisfaction and performance. Female nurses and those with higher incomes reported greater satisfaction, while higher patient loads were negatively correlated with performance indicators.

Levels of Job Satisfaction, Job Performance, and Work Engagement

This study identified moderate levels of job satisfaction, job performance, and work engagement among Jordanian critical care nurses. These findings align with previous regional studies, such as those by Alfuqaha, Al-Hairy, Al-Hemsi, Sabbah, Faraj and Assaf³⁷ and Alotaibi,³⁹ both of which reported moderate job satisfaction levels among nurses. Similarly, Kurt and Demirbag³⁸ observed that nurses across various clinical settings often express moderate satisfaction with their roles. However, these findings contrast with several international studies reporting low satisfaction levels^24,40 as well as others that reported high levels of satisfaction.^10,36 These differences in findings may be explained by variations in study settings and working environments across different healthcare systems.

Work engagement was also found to be moderate, consistent with Alali,⁴¹ who observed similar engagement levels among ICU nurses. However, variations in reported engagement may be influenced by the specific demands and dynamics of critical care environments. Notably, the frequency and quality dimensions of job performance also scored moderately high, suggesting that nurses perceive themselves as competent but with potential for improvement, especially in educational and interpersonal domains. This supports the assertion by Al-Ajarmeh, Rayan, Eshah and Al-Hamdan²¹ that while nurses in critical settings perform well, continuous professional development remains essential. Although numerous initiatives are being implemented within Ministry of Health settings to improve nurses’ work environments and job satisfaction, the persistence of only moderate levels suggests that these efforts may not yet be sufficiently addressing the root causes of dissatisfaction or disengagement. This highlights the need for a more targeted, evidence-based approach that considers organizational culture, workload, and professional development opportunities to enhance job satisfaction, work engagement, and overall performance among critical care nurses.

Relationship Among Job Satisfaction, Work Engagement, and Job Performance

The study revealed a weak positive correlation between job satisfaction and work engagement, affirming prior findings by Wei, Sewell, Woody and Rose²⁰ and Yildiz and Yildiz,¹⁴ which emphasized engagement as a motivational factor enhancing satisfaction and productivity. A moderate correlation between work engagement and job performance quality and frequency indicates that engagement plays a key role in enabling effective practice. Conversely, job satisfaction alone showed a weak correlation with job performance, particularly its frequency dimension, echoing Lu, Zhao and While⁹ and Bellali, Panayiotou, Liamopoulou, Mantziou, Minasidou and Manomenidis⁴² those who highlighted the complexity of performance predictors beyond satisfaction alone. This unexpected lack of association may be due to the complexity of performance, where intrinsic motivation (ie, engagement) is a more proximal driver of action than general satisfaction. Alternatively, performance frequency may be less sensitive to emotional states and more governed by organizational expectations or routine clinical protocols.

Notably, the findings underscore that nurses with high engagement are more likely to translate satisfaction into consistent and quality-driven performance. This supports the conclusions of Ghazawy, Mohamed, Sameh and and Refaei¹ and Bernales-Turpo, Quispe-Velasquez, Flores-Ticona, Saintila, Ruiz Mamani, Huancahuire-Vega, Morales-García and Morales-García,⁴³ who found engagement essential for task-oriented performance in demanding settings. Thus, job satisfaction is necessary but not sufficient—nurses must also be engaged and supported.

These insights suggest that while job satisfaction provides a foundational emotional state, it is the presence of sustained engagement that catalyzes actual performance outcomes in critical care environments. This distinction is especially important in high-stress settings, where emotional resilience and intrinsic motivation, hallmarks of work engagement, are critical for maintaining care quality. Therefore, interventions aiming to improve nurse performance should not focus solely on increasing satisfaction through extrinsic rewards, but also prioritize strategies that foster deep engagement, such as autonomy, recognition, professional growth, and meaningful involvement in decision-making processes.

The Role of Socio-Demographic Characteristics

The study found female nurses had higher job satisfaction than males, supporting the assertions of Lu, Lu, Gursoy and Neale¹² and Romem and Rozani⁴⁴ that gender differences in satisfaction may stem from differing expectations and adaptability. However, other demographic variables like age, marital status, and years of experience showed no significant relationship with performance, consistent with findings from Dilig-Ruiz, MacDonald, Demery Varin, Vandyk, Graham and Squires⁵ Bakker and Demerouti.⁴⁵

Importantly, patient load negatively correlated with both frequency and quality of job performance, reaffirming findings by Griffiths, Recio-Saucedo, Dall’Ora, Briggs, Maruotti, Meredith, Smith and Ball⁴⁶ and Dilig-Ruiz, MacDonald, Demery Varin, Vandyk, Graham and Squires⁵ on how increased workloads diminish care quality. Income was positively correlated with job satisfaction, validating Herzberg’s theory of hygiene factors and the assertion by Lu, Zhao and While⁹ that fair compensation supports employee morale.

These findings highlight the multifaceted nature of job satisfaction and performance among critical care nurses, where individual characteristics such as gender may influence emotional outcomes, while structural factors like workload and compensation have a more direct impact on practice quality. The absence of significant associations between demographic variables and performance reinforces the idea that systemic conditions, rather than personal attributes, play a greater role in shaping care outcomes. Consequently, nursing management and policymakers should focus on optimizing workload distribution and ensuring equitable compensation to foster both satisfaction and performance, particularly in high-demand clinical settings.

Moderating Role of Work Engagement

A key contribution of this study is confirming the moderating effect of work engagement in the relationship between job satisfaction and the frequency of job performance. Nurses with high engagement levels performed more consistently even when satisfaction was variable. This highlights the motivational power of engagement, echoing theories by Bakker and Demerouti⁴⁵ and findings by Morton, Bowers, Wessels, Koen and Tobias¹⁰ regarding the interplay of personality traits, resources, and engagement. Interestingly, engagement did not moderate the satisfaction-performance relationship in terms of quality, suggesting that while engagement drives consistency, quality may depend more on intrinsic and contextual factors such as autonomy, feedback, and clinical competence. This distinction underscores the complexity of performance drivers in nursing practice. While engagement enhances reliability and task completion, ensuring high-quality performance likely requires a combination of structural support, ongoing skill development, and a conducive work environment. These findings imply that to optimize both the consistency and quality of nursing care, interventions should be dual-pronged—fostering engagement through resource allocation and recognition, while also enhancing quality through clinical supervision, autonomy, and continuous professional development. This nuanced understanding can inform workforce strategies aimed at improving both the efficiency and excellence of patient care delivery.

The study addressed a literature gap in the Jordanian context, with adequate sample size and validated tools. However, the study has some limitations. First, its descriptive, cross-sectional design precludes establishing causal relationships between job satisfaction, work engagement, and job performance, and limits the ability to assess temporal stability in these associations. Second, convenience sampling from pre-selected Jordanian public hospitals restricts the generalizability of the findings to similar institutional settings and may not represent the broader nursing population across the country or region. Third, all measures were self-reported, raising the risk of social desirability bias and common-method variance, which could artificially inflate associations between variables. Future studies should incorporate supervisor ratings or objective performance data to address this concern. Fourth, while the moderation analysis revealed significant effects, interaction plots were not displayed due to space constraints; however, plotting was conducted as part of the analytical process and interpretations were based on the observed interaction effects. Fifth, although the Job Demands-Resources (JD-R) model and Social Exchange Theory were used to interpret the findings, they were not explicitly integrated into the statistical modeling. Finally, while the English version of the Job Satisfaction Survey (JSS) was used, cultural validity was established through expert panel review and pilot testing among Jordanian nurses to ensure conceptual and contextual relevance. Future research using longitudinal or cluster-randomized designs is recommended to strengthen causal inferences and external validity.

Conclusion

This study investigated the relationships among job satisfaction, work engagement, and job performance among critical care nurses in public hospitals in Jordan, revealing that while moderate levels were reported across all three variables, work engagement significantly moderated the relationship between job satisfaction and the frequency of job performance but not the quality. This nuanced finding highlights the pivotal role of engagement in sustaining consistent clinical effort, particularly when satisfaction alone may not be sufficient, while also pointing to the complexity of factors influencing performance quality. Moderate levels of satisfaction and engagement—though not critically low—may reflect latent risks of burnout or turnover, particularly in high-stress environments like critical care units. These findings indicate that while nurses perceive their work as meaningful, gaps in support, resources, or recognition may hinder optimal well-being and retention. The results have practical implications for nursing management and healthcare leadership. Nurse managers should implement targeted strategies that enhance both job satisfaction and engagement instead of general compensation increases, fair and transparent compensation systems, non-monetary incentives, workload balancing, and career advancement opportunities may better accommodate institutional constraints. Cultivating autonomy, professional respect, and involvement in unit-level decision-making can further elevate engagement. Maintaining high engagement levels can buffer fluctuations in job satisfaction and support stable performance, ultimately leading to improved patient outcomes and reduced turnover. Given the documented high nurse turnover rates in Jordan, these findings provide a timely and evidence-based framework for mitigating attrition by addressing the psychological and organizational factors that drive nurses’ intent to stay or leave. While this study did not directly assess patient outcomes or turnover, prior research suggests that enhanced job satisfaction and engagement are associated with improved care quality and reduced attrition. From a policy perspective, healthcare administrators and policymakers should prioritize nurse engagement as a key component of workforce planning and quality assurance. Institutional policies must aim to create supportive, communicative, and inclusive work cultures that empower nurses and promote a sense of professional value and ownership.

Furthermore, future research should adopt longitudinal designs to assess how job satisfaction, engagement, and performance evolve over time and identify effective interventions that sustain these variables. A deeper understanding of the mechanisms through which engagement enhances performance could inform leadership practices and educational programs tailored to high-acuity settings such as critical care units. Gaining insight into these psychological drivers is essential for designing targeted interventions that enhance workforce sustainability and improve patient care quality. The findings are expected to inform evidence-based human resource policies, engagement strategies, and clinical leadership models, thereby fostering a more satisfied, engaged, and high-performing nursing workforce in Jordan and similar healthcare settings.

Data Sharing Statement

The datasets generated and/or analysed during the current study are not publicly available due to participant confidentiality but are available from the corresponding author (Fadwa Alhalaiqa) on reasonable request.

Ethical Approval and Informed Consent

This study was approved by the Institutional Review Board of Zarqa University (Approval No: 25/2023). Informed consent was obtained from all participants prior to data collection. Participation was voluntary, and confidentiality was maintained throughout the study. All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments.

Acknowledgments

The authors of this study extends his appreciation to the Researchers Supporting Project Number (RSPD2025R1032), King Saud University, Riyadh, Saudi Arabia. The authors would like to thank the nursing departments and nurse managers in the participating hospitals for their support, as well as all the nurses who participated in the study. The authors acknowledge the partial funding from deanship of scientific Research at Zarqa University. Authors Also extend their appreciation to the deanship of Scientific Research at Northern Border University, Arar, KSA, for partial funding.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research received no external funding.

Disclosure

The authors report no conflicts of interest in this work.

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