Age group | Gender |
|
M | F | |
<30 | 3 | 22 |
30–40 | 7 | 40 |
41–50 | 14 | 34 |
51–60 | 15 | 74 |
61–65 | 1 | 3 |
Total | 40 | 173 |
In this study, a novel asymmetric image encryption scheme based on the Rivest-Shamir-Adleman (RSA) algorithm and Arnold transformation is proposed. First, the asymmetric public key RSA algorithm is used to generate the initial values for a quantum logistic map. Second, the parameters of the Arnold map are calculated. Then, Arnold scrambling operation is performed on the plain image to achieve the rough hiding of image information. Third, each row and each column of the image are taken as different units respectively and then exclusive-OR (XOR) diffusion is applied. Finally, the generated keystream is used to perform an end-to-start cyclic modulo diffusion operation for all rows and columns to produce the final cipher image. In addition, the keystream is related to the plain image, which can enhance the ability to resist chosen plaintext attack and known plaintext attack. The test results also show that the proposed encryption algorithm has strong plain sensitivity and key sensitivity.
Citation: Guodong Ye, Huishan Wu, Kaixin Jiao, Duan Mei. Asymmetric image encryption scheme based on the Quantum logistic map and cyclic modulo diffusion[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5427-5448. doi: 10.3934/mbe.2021275
[1] | Maryanna Klatt, Jacqueline Caputo, Julia Tripodo, Nimisha Panabakam, Slate Bretz, Yulia Mulugeta, Beth Steinberg . A highly effective mindfulness intervention for burnout prevention and resiliency building in nurses. AIMS Public Health, 2025, 12(1): 91-105. doi: 10.3934/publichealth.2025007 |
[2] | Wagih Mohamed Salama, Hazem Ahmed Khairy, Mohammad Gouda, Marwa Samir Sorour . Organizational cynicism and its relation to nurses' occupational burnout: Testing nurse managers' paradoxical leadership moderation effects. AIMS Public Health, 2025, 12(2): 275-289. doi: 10.3934/publichealth.2025017 |
[3] | Francesco Marcatto, Donatella Ferrante, Mateusz Paliga, Edanur Kanbur, Nicola Magnavita . Behavioral dysregulation at work: A moderated mediation analysis of sleep impairment, work-related stress, and substance use. AIMS Public Health, 2025, 12(2): 290-309. doi: 10.3934/publichealth.2025018 |
[4] | Margarida A. R. Tomás, Marisa R. Soares, Joaquim M. Oliveira-Lopes, Luís M. M. Sousa, Vânia L. D. Martins . The influence of nursing handover on nurses' mental health: A scoping review. AIMS Public Health, 2025, 12(1): 106-123. doi: 10.3934/publichealth.2025008 |
[5] | Thiresia Sikioti, Afroditi Zartaloudi, Despoina Pappa, Polyxeni Mangoulia, Evangelos C. Fradelos, Freideriki Eleni Kourti, Ioannis Koutelekos, Evangelos Dousis, Nikoletta Margari, Areti Stavropoulou, Eleni Evangelou, Chrysoula Dafogianni . Stress and burnout among Greek critical care nurses during the COVID-19 pandemic. AIMS Public Health, 2023, 10(4): 755-774. doi: 10.3934/publichealth.2023051 |
[6] | Mohammed Adeeb Shahin, Sami Abdo Radman Al-Dubai, Duoaa Seddiq Abdoh, Abdullah Saud Alahmadi, Ahmed Khalid Ali, Tamer Hifnawy . Burnout among nurses working in the primary health care centers in Saudi Arabia, a multicenter study. AIMS Public Health, 2020, 7(4): 844-853. doi: 10.3934/publichealth.2020065 |
[7] | Vassiliki Diamantidou, Evangelos C. Fradelos, Athina Kalokairinou, Daphne Kaitelidou, Petros Galanis . Comparing predictors of emotional intelligence among medical and nursing staff in national health system and military hospitals: A cross-sectional study in Greece. AIMS Public Health, 2025, 12(3): 657-674. doi: 10.3934/publichealth.2025034 |
[8] | Vasileios Tzenetidis, Aristomenis Kotsakis, Mary Gouva, Konstantinos Tsaras, Maria Malliarou . Examining psychosocial risks and their impact on nurses' safety attitudes and medication error rates: A cross-sectional study. AIMS Public Health, 2025, 12(2): 378-398. doi: 10.3934/publichealth.2025022 |
[9] | Nicola Magnavita, Francesco Marcatto, Igor Meraglia, Giacomo Viti . Relationships between individual attitudes and occupational stress. A cross-sectional study. AIMS Public Health, 2025, 12(2): 557-578. doi: 10.3934/publichealth.2025030 |
[10] | Ilenia Piras, Igor Portoghese, Massimo Tusconi, Federica Minafra, Mariangela Lecca, Giampaolo Piras, Paolo Contu, Maura Galletta . Professional and personal experiences of workplace violence among Italian mental health nurses: A qualitative study. AIMS Public Health, 2024, 11(4): 1137-1156. doi: 10.3934/publichealth.2024059 |
In this study, a novel asymmetric image encryption scheme based on the Rivest-Shamir-Adleman (RSA) algorithm and Arnold transformation is proposed. First, the asymmetric public key RSA algorithm is used to generate the initial values for a quantum logistic map. Second, the parameters of the Arnold map are calculated. Then, Arnold scrambling operation is performed on the plain image to achieve the rough hiding of image information. Third, each row and each column of the image are taken as different units respectively and then exclusive-OR (XOR) diffusion is applied. Finally, the generated keystream is used to perform an end-to-start cyclic modulo diffusion operation for all rows and columns to produce the final cipher image. In addition, the keystream is related to the plain image, which can enhance the ability to resist chosen plaintext attack and known plaintext attack. The test results also show that the proposed encryption algorithm has strong plain sensitivity and key sensitivity.
Nursing, which is known for its indispensable role in healthcare, is a profession that comes with multifaceted challenges. The intricate nature of healthcare settings, characterized by high workloads, time pressures, and emotional demands, predisposes nurses to elevated levels of stress [1],[2]. Occupational stress among nurses can have profound consequences for both nurses' well-being and patient care, as well as organizational effectiveness. One notable consequence of occupational stress is its detrimental effect on nurses' physical and mental health. Prolonged exposure to high levels of stress can lead to symptoms of burnout, including emotional exhaustion, depersonalization, and a reduced sense of personal accomplishment [3],[4]. This can result in increased absenteeism, turnover rates, and decreased job satisfaction among nurses, ultimately undermining their ability to perform their duties effectively and compromising the quality of care provided to patients [5]–[8]. Additionally, stress-related health issues, such as cardiovascular disease, musculoskeletal disorders, and mental health disorders like anxiety and depression, can further impair nurses' ability to fulfill their professional responsibilities [9]–[12]. Thus, the consequences of stress in nurses not only affect their own well-being but also have far-reaching implications for the quality and safety of healthcare delivery [13],[14].
Within the realm of nursing, stressors can be categorized into two broad distinct domains: Operational (also called content-factors) and organizational (also called context-factors) [15],[16]. Operational stressors encompass the immediate challenges and demands inherent in nursing practice, including high patient acuity, shift work, exposure to traumatic events, and dealing with patient suffering and death [16],[17]. In contrast, organizational stressors transcend individual tasks and encompass aspects of the work environment and organizational culture. These stressors arise from factors such as inadequate staffing levels, lack of control over one's tasks, unclear job roles, and the presence of conflict with colleagues [15].
Unlike operational stressors, which are inherent to the nature of nursing practice, organizational stressors represent a modifiable aspect of the work environment, offering opportunities for intervention and mitigation by healthcare organizations. Moreover, while both operational and organizational stressors exert significant impacts on nurses' well-being and job satisfaction, numerous studies showed that nurses frequently cite organizational factors as the main sources of stress [14]–[19]. Addressing organizational stressors is therefore essential for promoting both nurse well-being and optimal patient care outcomes.
One notable framework that has gained prominence in addressing organizational stressors is the Health and Safety Executive (HSE) Management Standards approach [20]. Developed by the HSE in the United Kingdom, this approach provides a comprehensive framework for managing work-related stress by identifying seven key organizational stressors and implementing targeted interventions. According to this approach, the seven primary areas of work design that are crucial for promoting employee well-being and preventing stress are the following: Demands, Control, Managers' support, Peer support, Relationships, Role, and Change. The Demands area pertains to the extent and nature of the workload, including issues such as workload volume, pace of work, and the cognitive and emotional demands placed on employees. Control refers to the degree of autonomy and decision-making authority employees have over their work tasks and how much input they have in decision-making processes. Support involves the provision of adequate resources, encouragement, and assistance from supervisors (Managers' support) and colleagues (Peer support), to enable employees to carry out their work effectively. The Relationships dimension encompasses the quality of relationships within the workplace, as well as the presence of conflict or bullying. Role concerns clarity and understanding of job roles and responsibilities, and the presence/absence of conflicting roles. Last, change addresses the extent to which organizational changes are managed effectively, including communication, consultation, and employee involvement in the change process.
By systematically assessing these organizational areas, organizations can develop targeted interventions to address specific stressors and promote employee well-being. For this purpose, the HSE has developed the HSE-Management Standards Indicator Tool (HSE-MS IT) questionnaire [21]. Several studies have already demonstrated its robust psychometric properties [22], and how each scale is sensitive to different psychological and physical outcomes, including job satisfaction [23],[24], perceived stress at work [25], job-related anxiety and depression, musculoskeletal pain, hypertension and gastrointestinal disorders [26],[27], and work ability [28].
By adopting the HSE Management Standards approach, healthcare organizations can proactively manage organizational stressors within the nursing profession, thereby promoting a healthier work environment and supporting nurses in delivering high-quality care while safeguarding their own well-being [29],[30].
In this study, we aim to explore the associations between exposure to organizational stressors, as measured by HSE-MS IT, and a spectrum of psychosomatic complaints among nurses working in a hospital in mid-sized city in Italy. Psychosomatic complaints encompass a diverse array of physical symptoms influenced by psychological factors [31],[32], and are known to be strongly associated with work-related stress [33].
This way, we aim to shed light on the pathways through which work-related stress influences nurses' well-being. Identifying which specific organizational stressors play a significant role in the prevalence of psychosomatic complaints holds profound implications for the formulation and implementation of targeted stress management interventions, thereby bolstering nurses' well-being and improving organizational effectiveness.
For five months, from October 2022 to March 2023, all nurses working in the same hospital located in a medium-sized city (with approximately 200.000 inhabitants) in northeastern Italy were asked to take part in the study during routine preventive occupational medicine consultation. The majority of nurses accepted the invitation to participate (acceptance rate = 92%), and written informed consent was collected from each participant. A research assistant measured participants' weight and height for body mass index [BMI] calculation. Subsequently, participants were asked to complete a paper-and-pencil questionnaire and return it in a closed urn to ensure anonymity.
The study was approved by the Ethical Committee of Friuli-Venezia Giulia (ID: 16810) and was conducted in accordance with the principles outlined in the Helsinki Declaration.
Participants received a booklet divided into two sections. The first section contained the Italian version of the HSE-MS IT [34], a 35-item questionnaire designed to assess exposure to organizational stress factors based on the HSE Management Standards framework [21]. The HSE-MS IT considers a six-month time window prior to measurement and consists of seven scales: Demands (8 items), Control (6 items), Managers' support (5 items), Peer support (4 items), Relationships (4 items), Role (5 items), and Change (3 items). Higher scores on the HSE-MS IT scales indicate a lower risk of stress.
The second section included eight items measuring the prevalence of a set of psychosomatic complaints (palpitations, sleep disorders, depression, irritability, anxiety, physical and mental tiredness, headache, and osteoarticular pain) experienced over the last six months. These complaints are commonly associated with work-related stress [25],[26],[35] and were assessed using a five-point scale (ranging from never to always).
Mean scores and standard deviations were calculated for each of the seven HSE-MS IT scales, and compared with Italian benchmark data [36]. Descriptive statistics were also provided for nurses' psychosomatic complaints. To assess associations between HSE-MS IT scales and psychosomatic complaints, Pearson correlations were calculated between the HSE-MS IT scales and the psychosomatic complaints. Subsequently, hierarchical logistic regressions were conducted with each complaint as outcome variables and the HSE-MS IT scales as predictors, after controlling for gender, age group, and BMI. HSE-MS IT scores below the 20th percentile of the benchmark data were coded as 1 to indicate a high stress risk, while scores above the 20th percentile were coded as 0. Psychosomatic complaints scores were dichotomized to distinguish between nurses reporting low prevalence (1–3, coded as 0) and high prevalence (4–5, coded as 1). This way, Odds Ratio (OR) and their respective 95% Confidence Intervals (95% CI) were calculated for each psychosomatic complaint, adjusting for the effects of gender, age, and BMI. Multicollinearity was assessed prior to data analysis, with a variance inflation factor (VIF) of less than 5 set as the cutoff value. All analyses were conducted using Jamovi software.
The final sample consisted of 215 nurses, and their demographic characteristics are reported in Table 1.
Age group | Gender |
|
M | F | |
<30 | 3 | 22 |
30–40 | 7 | 40 |
41–50 | 14 | 34 |
51–60 | 15 | 74 |
61–65 | 1 | 3 |
Total | 40 | 173 |
Note: Two participants did not report their gender
Descriptive statistics of the HSE-MS IT scales are provided in Table 2. Compared to Italian benchmark data, the average scores for Demands, Managers' support, Relationships, and Change fell between the 20th and 50th percentiles (labeled as ‘‘Clear need for improvement”), while scores for the others scales were above the 50th percentile (labeled as ‘‘Good, but need for improvement”).
HSE-MS IT scale | Mean (SD) | Benchmark comparison |
Demands | 3.28 (0.60) | <50th percentile |
Control | 3.55 (0.66) | >50th percentile |
Managers' support | 3.79 (0.98) | <50th percentile |
Peer support | 4.13 (0.62) | >50th percentile |
Relationships | 3.93 (0.75) | <50th percentile |
Role | 4.52 (0.47) | >50th percentile |
Change | 3.66 (0.82) | <50th percentile |
As shown in Table 3, sleep disorders and tiredness (physical and mental) were the most prevalent complaints, with approximately 40% of the nurses reporting experiencing them often or always. Palpitations, depression and anxiety were instead the least prevalent complaints, with fewer than 20% of nurses reporting a high frequency.
Psychosomatic symptom | Mean (SD) | % of scores ≥ 4 |
Palpitations | 2.23 (1.05) | 13.5% |
Sleep disorders | 3.04 (1.18) | 39.8% |
Depression | 2.38 (1.11) | 17.5% |
Irritability | 2.62 (1.04) | 21.9% |
Anxiety | 2.24 (1.14) | 15.8% |
Physical and mental tiredness | 3.17 (1.07) | 43.3% |
Headache | 2.38 (1.17) | 20.3% |
Osteoarticular pain | 2.49 (1.29) | 26.8% |
Correlational analyses (Table 4) revealed significant associations between psychosomatic complaints and the organizational stressors measured by the HSE-MS Indicator Tool scales.
Project | D | C | MS | PS | RE | RO | C |
Palpitations | -0.21** | -0.02 | -0.03 | -0.05 | -0.24*** | -0.06 | -0.14* |
Sleep disorders | -0.18** | -0.13 | -0.27*** | -0.20** | -0.34*** | -0.16* | -0.24*** |
Depression | -0.36*** | -0.15* | -0.23*** | -0.19** | -0.39*** | -0.19** | -0.27*** |
Irritability | -0.39*** | -0.24*** | -0.24*** | -0.21** | -0.42*** | -0.20** | -0.30*** |
Anxiety | -0.21*** | -0.12 | -0.19** | -0.16* | -0.32*** | -0.29*** | -0.23*** |
Physical and mental tiredness | -0.42*** | -0.26*** | -0.17* | -0.16* | -0.39*** | -0.27*** | -0.26*** |
Headache | -0.09 | -0.05 | -0.06 | -0.16* | -0.14* | -0.09 | -0.12 |
Osteoarticular pain | -0.19** | 0.01 | -0.08 | -0.15* | -0.19** | -0.16* | -0.07 |
Note: D = Demands, C = Control, MS = Managers' support, PS = Peer support, RE = Relationships, RO = Role, C = Change. *p < 0.05, **p < 0.01, ***p < 0.001.
To provide a more nuanced understanding of the contribution of each organizational stressor to psychosomatic complaints, multiple regression analyses were conducted, controlling for gender, age group, and BMI.
Table 5 presents the ORs between exposure to organizational stressors and psychosomatic complaints. The Relationships factor was associated with an increased risk of experiencing palpitations (3.89 times), irritability (4.85 times), anxiety (3.38 times), physical and mental tiredness (7.09 times), and headache (3.04 times). Additionally, Demands increased the risk of experiencing irritability (2.51 times) and physical and mental tiredness (2.65 times), while Managers' support was a significant risk factor for depression (2.74 times). No significant associations were found between organizational stress factors and sleep disorders or osteoarticular pain.
Predictors | Palpitations | Sleep disorders | Depression | Irritability | Anxiety | Physical and mental tiredness | Headache | Osteoarticular pain |
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Age group | 0.67* (0.45–0.98) | 1.34 (1.00–1.78) | 0.87 (0.61–1.25) | 0.76 (0.54–1.06) | 0.87 (0.60–1.27) | 1.16 (0.85–1.56) | 0.85 (0.60–1.20) | 2.35*** (1.56–3.55) |
Gender (female) | 5.45* (1.11–26.66) | 2.18 (0.95–5.02) | 1.74 (0.56–5.34) | 2.31 (0.73–7.31) | 2.34 (0.68–8.09) | 2.63* (1.05–6.59) | 5.65* (1.50–21.20) | 4.16* (1.30–13.31) |
BMI | 2.88* (1.13–7.34 | 1.52 (0.80–2.87) | 1.84 (0.82–4.13) | 0.84 (0.38–1.85) | 2.21 (0.95–5.14) | 2.57* (1.30–5.09) | 3.63** (1.64–8.04) | 2.24* (1.09–4.63) |
Demands | 0.66 (0.24–1.82) | 1.37 (0.69–2.71) | 2.01 (0.87–4.64) | 2.51* (1.14–5.52) | 1.36 (0.57–3.26) | 2.65** (1.29–5.42) | 0.75 (0.32–1.75) | 1.92 (0.88–4.17) |
Control | 0.75 (0.18–3.04) | 0.94 (0.37–2.38) | 0.49 (0.13–1.78) | 0.51 (0.16–1.63) | 0.50 (0.13–1.92) | 2.67 (0.91–7.79) | 0.81 (0.25–2.63) | 1.35 (0.45–4.00) |
Managers' support | 1.57 (0.49–5.05) | 2.02 (0.87–4.71) | 2.74* (1.01–7.44) | 1.55 (0.57–4.17) | 1.68 (0.58–4.83) | 0.52 (0.19–1.42) | 1.19 (0.43–3.33) | 0.62 (0.22–1.76) |
Peer support | 0.48 (0.05–4.93) | 2.07 (0.42–10.12) | 1.24 (0.24–6.43) | 0.29 (0.04–1.86) | 0.50 (0.08–3.16) | 0.61 (0.10–3.60) | 1.23 (0.24–6.26) | 0.35 (0.06–2.22) |
Relationships | 3.89* (1.10–13.79) | 1.58 (0.59–4.27) | 1.36 (0.43–4.29) | 4.85** (1.66–14.19) | 3.38* (1.11–10.29) | 7.09** (1.93–26.03) | 3.04* (1.02–9.08) | 1.53 (0.49–4.80) |
Role | 0.49 (0.09–2.60) | 0.74 (0.26–2.06) | 2.09 (0.70–6.31) | 2.62 (0.88–7.78) | 2.72 (0.89–8.31) | 2.22 (0.68–7.31) | 1.06 (0.33–3.42) | 1.54 (0.50–4.68) |
Change | 0.70 (0.20–2.45) | 1.36 (0.80–2.87) | 0.83 (0.29–2.39) | 0.91 (0.34–2.47) | 1.02 (0.34–3.02) | 1.33 (0.51–3.49) | 1.04 (0.37–2.92) | 0.92 (0.32–2.67) |
The recognition of the negative effects of organizational stressors, both on nurses' health and on the quality of patient care and organizational effectiveness, underscores the relevance of investigating their implications comprehensively. Addressing these challenges necessitates a nuanced understanding of the specific organizational stressors prevalent in nursing contexts and their relationship with health outcomes.
We investigated the associations between exposure to the seven organizational stressors of the HSE Management Standards framework, and a range of psychosomatic complaints among nurses in a medium-sized city hospital in northeastern Italy. Consistent with previous research, our results revealed significant associations between various organizational stressors and the prevalence of psychosomatic complaints [26],[27]. Specifically, the Relationships factor emerged as a significant predictor of several psychosomatic complaints, including palpitations, irritability, anxiety, physical and mental tiredness, and headache. This result underscores the critical role of interpersonal relationships within the workplace in influencing nurses' mental and physical health outcomes [10],[37],[38]. Additionally, Demands and Managers' support were identified as significant predictors of specific psychosomatic complaints (irritability and physical and mental tiredness, and depression, respectively), further highlighting the multifaceted nature of organizational stress and the need for targeted interventions to address diverse stressors in the nursing profession.
These results are in line with the broader literature on organizational stress and its impact on healthcare professionals' well-being. For example, studies among other healthcare professionals, such as radiologists, have similarly highlighted the detrimental effects of organizational stressors on mental and physical health outcomes [39],[40]. By acknowledging the broader literature on organizational stress and its impact on healthcare professionals, we gain a deeper understanding of the complex dynamics at play in healthcare settings. Moving forward, interventions aimed at mitigating organizational stressors should consider the unique challenges faced by different healthcare professionals and prioritize the creation of supportive work environments that promote well-being and optimal patient care delivery.
The strengths of this study include the use of a well-established measurement tool, the HSE-MS IT, to assess organizational stressors, and a comprehensive assessment of a spectrum of psychosomatic complaints commonly associated with work-related stress. Furthermore, the large sample size and high participation rate enhance the reliability of our findings.
However, some limitations need to be acknowledged. Initially, the cross-sectional design precludes causal inference, and longitudinal studies are needed to establish temporal relationships between organizational stressors and psychosomatic complaints. Additionally, the use of self-report measures introduces the potential for response bias, and future research could benefit from incorporating objective measures or observational data to corroborate self-reported findings. Furthermore, it is plausible that the COVID-19 pandemic exacerbated stress among nurses. Although data collection occurred after the onset of the pandemic, nurses directly experienced its effects, which included an unforeseen surge in workload and uncertainty within what they perceived as a hostile environment [41]–[43]. Consequently, the obtained results may have been influenced by pandemic-related fatigue, potentially exacerbating perceptions of workload and straining professional relationships. Another limitation of this study is the potential influence of social desirability bias, wherein participants may have responded in a manner they deemed socially acceptable than providing honest or accurate responses, in order to portray themselves and their organization in a favorable light. This bias could be particularly relevant in the healthcare sector, given the numerous challenges and criticisms faced by healthcare professionals during the COVID-19 pandemic [44]. Future research could employ alternative methodologies, such as mixed methods incorporating both questionnaires and interviews or focus groups, to mitigate this bias and further investigate the impact of organizational stressors on nurses' well-being.
Despite these limitations, our study provides valuable insights into the complex interplay between organizational stressors and nurses' health outcomes. By identifying specific stressors that contribute to psychosomatic complaints, healthcare organizations can implement targeted interventions to mitigate these stressors and promote a healthier work environment for nurses. In their systematic review, Cohen and colleagues [45] found that the majority of interventions aimed at improving the well-being of healthcare workers employed both individual and organizational approaches. At the individual level, interventions were predominantly focused on secondary prevention strategies, such as stress management techniques including mindfulness-based practices, meditation, yoga, acupuncture, and fostering a positive mindset. Organizational-level interventions encompassed measures to alleviate workload, encourage job crafting, and establish peer support networks. Notably, the authors reported that most studies documented positive outcomes, including enhancements in well-being, increased work engagement, and reductions in burnout, perceived stress, anxiety, and depression symptoms.
In conclusion, our study underscores the importance of addressing organizational stressors in nursing practice, including those related to interpersonal relationships within the workplace. The significant associations between the Relationships factor and various psychosomatic complaints highlight the critical role of fostering positive social interactions in promoting nurses' mental and physical well-being. Thus, by prioritizing the creation of supportive work environments and implementing evidence-based interventions, healthcare organizations can foster a culture of well-being among nurses, ultimately enhancing the quality and safety of healthcare delivery.
The authors declare no Artificial Intelligence (AI) tools have been used in the creation of this article.
[1] |
G. D. Ye, K. X. Jiao, H. S. Wu, C. Pan, X. L. Huang, An asymmetric image encryption algorithm based on a fractional-order chaotic system and the RSA public-key cryptosystem, Int. J. Bifurcat. Chaos, 30 (2020), 2050233. doi: 10.1142/S0218127420502338
![]() |
[2] |
H. J Liu, Y. Q. Zhang, A. Kadir, Y. Q. Xu, Image encryption using complex hyper chaotic system by injecting impulse into parameters, App. Math. Comput., 360 (2019), 83-93. doi: 10.1016/j.amc.2019.04.078
![]() |
[3] | E. A. Albahrani, A. A. Maryoosh, S. H. Lafta, Block image encryption based on modified playfair and chaotic system, J. Inf. Secur. Appl., 51 (2020), 102445. |
[4] |
S. S. Yu, N. R. Zhou, L. H. Gong, Z. Nie, Optical image encryption algorithm based on phase-truncated short-time fractional Fourier transform and hyper-chaotic system, Opt. Laser Eng., 124 (2020), 105816. doi: 10.1016/j.optlaseng.2019.105816
![]() |
[5] |
X. J. Tong, M. Zhang, Z. Wang, J. Ma, A joint color image encryption and compression scheme based on hyper-chaotic system, Nonlinear Dyn., 84 (2016), 2333-2356. doi: 10.1007/s11071-016-2648-x
![]() |
[6] | X. J. Kang, Z. H. Guo, A new color image encryption scheme based on DNA encoding and spatiotemporal chaotic system, Signal Process. Image Commun., 80 (2020), 15670. |
[7] |
S. M. Ismail, L. A. Said, A. G. Radwan, A. H. Madian, M. F. Abu-ElYazeed, A novel image encryption system merging fractional-order edge detection and generalized chaotic maps, Signal Process., 167 (2020), 107280. doi: 10.1016/j.sigpro.2019.107280
![]() |
[8] | S. E. Borujeni, M. Eshghi, Chaotic image encryption system using phase-magnitude transformation and pixel substitution, Telecommun. Syst., 52 (2013), 525-537. |
[9] |
D. S. Malik, T. Shah, Color multiple image encryption scheme based on 3D-chaotic maps, Math. Comput. Simulat., 178 (2020), 646-666. doi: 10.1016/j.matcom.2020.07.007
![]() |
[10] |
M. Alawida, A. Samsudin, J. S. Teh, R. S. Alkhawaldeh, A new hybrid digital chaotic system with applications in image encryption, Signal Process., 160 (2019), 45-58. doi: 10.1016/j.sigpro.2019.02.016
![]() |
[11] |
H. J. Liu, A. Kadir, J. Liu, Color pathological image encryption algorithm using arithmetic over Galois field and coupled hyper chaotic system, Opt. Laser Eng., 122 (2019), 123-133. doi: 10.1016/j.optlaseng.2019.05.027
![]() |
[12] |
W. Feng, Y. G. He, H. M. Li, C. L. Li, Image encryption algorithm based on discrete logarithm and memristive chaotic system, Eur. Phys. J-Spec. Top., 228 (2019), 1951-1967. doi: 10.1140/epjst/e2019-800209-3
![]() |
[13] |
Z. J. Huang, S. Cheng, L. H. Gong, N. R. Zhou, Nonlinear optical multi-image encryption scheme with two-dimensional linear canonical transform, Opt. Laser Eng., 124 (2020), 105821. doi: 10.1016/j.optlaseng.2019.105821
![]() |
[14] | K. A. Patro, B. Acharya, An efficient colour image encryption scheme based on 1-D chaotic maps, J. Inf. Secur. Appl., 46 (2019), 23-41. |
[15] |
X. Y. Wang, S. Gao, Image encryption algorithm for synchronously updating Boolean networks based on matrix semi-tensor product theory, Inf. Sci., 507 (2020), 16-36. doi: 10.1016/j.ins.2019.08.041
![]() |
[16] |
Y. J. Xian, X. Y. Wang, Fractal sorting matrix and its application on chaotic image encryption, Inf. Sci., 547 (2021), 1154-1169. doi: 10.1016/j.ins.2020.09.055
![]() |
[17] |
H. G. Zhu, L.W. Dai, Y. T. Liu, L. J. Wu, A three-dimensional bit-level image encryption algorithm with Rubik' s cube method, Math. Comput. Simulat., 185 (2021), 754-770. doi: 10.1016/j.matcom.2021.02.009
![]() |
[18] |
H. G. Zhu, X. D. Zhang, H. Yu, C. Zhao, Z. L. Zhu, An image encryption algorithm based on compound homogeneous hyper-chaotic system, Nonlinear Dyn., 89 (2017), 61-79. doi: 10.1007/s11071-017-3436-y
![]() |
[19] |
X. Y. Wang, J. J. Yang, A privacy image encryption algorithm based on piecewise coupled map lattice with multi dynamic coupling coefficient, Inf. Sci., 569 (2021), 217-240. doi: 10.1016/j.ins.2021.04.013
![]() |
[20] |
H. G. Zhu, Y. R. Zhao, Y. J. Song, 2D logistic-modulated-sine-coupling-logistic chaotic map for image encryption, IEEE Access, 7 (2019), 14081-14098. doi: 10.1109/ACCESS.2019.2893538
![]() |
[21] |
P. Singh, A. K. Yadav, K. Singh, Phase image encryption in the fractional Hartley domain using Arnold transform and singular value decomposition, Opt. Laser Eng., 91 (2017), 187-195. doi: 10.1016/j.optlaseng.2016.11.022
![]() |
[22] |
Z. J. Liu, L. Xu, T. Liu, H. Chen, P. F. Li, C. Lin, et al., Color image encryption by using Arnold transform and color-blend operation in discrete cosine transform domains, Opt. Commun., 284 (2011), 123-128. doi: 10.1016/j.optcom.2010.09.013
![]() |
[23] |
L. S. Sui, B. Gao, Color image encryption based on gyrator transform and Arnold transform, Opt. Laser Technol., 48 (2013), 530-538. doi: 10.1016/j.optlastec.2012.11.020
![]() |
[24] |
W. Chen, C. Quan, C. J. Tay, Optical color image encryption based on Arnold transform and interference method, Opt. Commun., 282 (2009), 3680-3685. doi: 10.1016/j.optcom.2009.06.014
![]() |
[25] |
W. Chen, X. D. Chen, Optical image encryption using multilevel Arnold transform and noninterferometric imaging, Opt. Eng., 50 (2011), 117001-117005. doi: 10.1117/1.3643724
![]() |
[26] |
X. Y. Wang, L. Feng, H Y Zhao, Fast image encryption algorithm based on parallel computing system, Inf. Sci., 486 (2019), 340-358. doi: 10.1016/j.ins.2019.02.049
![]() |
[27] | N. R. Zhou, T. X. Hua, L. H. Gong, D. J. Pei, Q. H. Liao, Quantum image encryption based on generalized Arnold transform and double random-phase encoding, Quantum Inf. Process., 14 (2014), 1193-1213. |
[28] | G. D. Ye, C. Pan, Y. X. Dong, K. X. Jiao, X. L. Huang, A novel multi-image visually meaningful encryption algorithm based on compressive sensing and Schur decomposition, Trans. Emerg. Telecommun. Technol., 32 (2021), e4071. |
[29] |
R. Ponuma, R. Amutha, Encryption of image data using compressive sensing and chaotic system, Multimed. Tools Appl., 78 (2019), 11857-11881. doi: 10.1007/s11042-018-6745-3
![]() |
[30] | Y. X. Dong, X. L. Huang, G. D. Ye, Visually meaningful image encryption scheme based on DWT and schur decomposition, Secur. Commun. Networks, 2021 (2021), 6677325. |
[31] | C. Wu, Y. Wang, Y. Chen, J. Wang, Q. H. Wang, Asymmetric encryption of multiple-image based on compressed sensing and phase-truncation in cylindrical diffraction domain, Opt. Commun., 413 (2019), 203-209. |
[32] |
W. Qin, X. Peng, Asymmetric cryptosystem based on phase-truncated fourier Transforms, Opt. Lett., 35 (2010), 118-120. doi: 10.1364/OL.35.000118
![]() |
[33] |
C. Wu, K. Y. Hu, Y. Wang, J. Wang, Scalable asymmetric image encryption based on phase-truncation in cylindrical diffraction domain, Opt. Commun., 448 (2019), 26-32. doi: 10.1016/j.optcom.2019.05.009
![]() |
[34] |
J. H. Wu, X. F. Liao, B. Yang, Color image encryption based on chaotic systems and elliptic curve ElGamal scheme, Signal Process., 141 (2017), 109-124. doi: 10.1016/j.sigpro.2017.04.006
![]() |
[35] |
G. D. Ye, K. X. Jiao, X. L. Huang, Quantum logistic image encryption algorithm based on SHA-3 and RSA, Nonlinear Dyn., 104 (2021), 2807-2827. doi: 10.1007/s11071-021-06422-2
![]() |
[36] |
S. K. Rajput, N. K. Nishchal, Image encryption based on interference that uses fractional Fourier domain asymmetric keys, Appl. Opt., 51 (2012), 1446-1452. doi: 10.1364/AO.51.001446
![]() |
[37] |
G. H. Ren, J. N. Han, J. H. Fu, M. G. Shan, Asymmetric image encryption using phase-truncated discrete multiple-parameter fractional Fourier transform, Opt. Rev., 25 (2018), 701-707. doi: 10.1007/s10043-018-0464-x
![]() |
[38] |
X. L. Chai, H. Y. Wu, Z. H. Gan, D. J. Han, Y. S. Zhang, Y. R. Chen, An efficient approach for encrypting double color images into a visually meaningful cipher image using 2D compressive sensing, Inf. Sci., 556 (2021), 305-340. doi: 10.1016/j.ins.2020.10.007
![]() |
[39] |
X. L. Chai, H. Y. Wu, Z. H. Gan, Y. S. Zhang, Y. R. Chen, Hiding cipher-images generated by 2-D compressive sensing with a multi-embedding strategy, Signal Process., 171(2020), 107525. doi: 10.1016/j.sigpro.2020.107525
![]() |
[40] |
A. Akhshani, A. Akhavan, S. Lim, Z. Hassan, An image encryption scheme based on quantum logistic map, Commun. Nonlinear Sci. Numer. Simul., 17 (2012), 4653-4661. doi: 10.1016/j.cnsns.2012.05.033
![]() |
[41] | A. A. El-Latif, L. Li, N. Wang, Q. Han, X. M. Niu, A new approach to chaotic image encryption based on quantum chaotic system, exploiting color spaces, Signal Process., 93 (2013), 2986-3000. |
[42] | S. kuchaki, A novel color image encryption algorithm based on spatial permutation and quantum chaotic map, Nonlinear Dyn., 81 (2015), 511-529. |
[43] |
J. Zhang, D. Huo, Image encryption algorithm based on quantum chaotic maps and DNA coding, Multimed. Tools Appl., 78 (2019), 15605-15621. doi: 10.1007/s11042-018-6973-6
![]() |
[44] |
Y. Q. Zhang, X. Y. Wang, A symmetric image encryption algorithm based on mixed linear-nonlinear coupled map lattice, Inf. Sci., 273 (2014), 329-351. doi: 10.1016/j.ins.2014.02.156
![]() |
[45] |
X. Y. Wang, S. Gao, Image encryption algorithm based on the matrix semi-tensor product with a compound secret key produced by a Boolean network, Inf. Sci., 539 (2020), 195-214. doi: 10.1016/j.ins.2020.06.030
![]() |
[46] |
G. Z. Hu, B. B. Li, Coupling chaotic system based on unit transform and its applications in image encryption, Signal Process., 178 (2021), 107790. doi: 10.1016/j.sigpro.2020.107790
![]() |
[47] |
X. L. Chai, J. Q. Bi, Z. H. Gan, X. X. Liu, Y. S. Zhang, Y. R. Chen, Color image compression and encryption scheme based on compressive sensing and double random encryption strategy, Signal Process., 176 (2020), 107684. doi: 10.1016/j.sigpro.2020.107684
![]() |
[48] |
Y. Q. Zhang, Y. He, P. Li, X. Y. Wang, A new color image encryption scheme based on 2DNLCML system and genetic operations, Opt. Laser Eng., 128 (2020), 106040. doi: 10.1016/j.optlaseng.2020.106040
![]() |
[49] |
X. Y. Wang, X. M. Qin, C. M. Liu, Color image encryption algorithm based on customized globally coupled map lattices, Multimedia Tools Appl., 78 (2019), 6191-6209. doi: 10.1007/s11042-018-6326-5
![]() |
[50] |
Z. H. Gan, X. L. Chai, M. H. Zhang, Y. Lu, A double color image encryption scheme based on three-dimensional brownian motion, Multimedia Tools Appl., 77 (2018), 27919-27953. doi: 10.1007/s11042-018-5974-9
![]() |
1. | Francesco Marcatto, Donatella Ferrante, Lisa Di Blas, Francesca Larese Filon, Adapting the HSE-MS Indicator Tool for Academia: A Psychometric Evaluation of the Academic Teacher Stress Indicator Tool in Italian, 2024, 115, 2532-1080, e2024041, 10.23749/mdl.v115i6.16294 | |
2. | V. E. Ironosov, D. O. Ivanov, K. V. Pshenisnov, Yu. S. Aleksandrovich, A. V. Agafoniva, Occupational stress as a risk factor for cardiovascular accidents in medical staff of anesthesiology and intensive care units (review), 2025, 22, 2541-8653, 139, 10.24884/2078-5658-2025-22-2-139-148 | |
3. | Amel Kchaou, Rania Abdelhedi, Feriel Dhouib, Nada Kotti, Imen Sellami, Cyrine Ben Hammouda, Kaouthar Jmal Hammami, Mohamed Larbi Masmoudi, Mounira Hajjaji, An example of a potential predictive model for psychological distress among nursing staff members, 2025, 1051-9815, 10.1177/10519815251335781 |
Age group | Gender |
|
M | F | |
<30 | 3 | 22 |
30–40 | 7 | 40 |
41–50 | 14 | 34 |
51–60 | 15 | 74 |
61–65 | 1 | 3 |
Total | 40 | 173 |
Note: Two participants did not report their gender
HSE-MS IT scale | Mean (SD) | Benchmark comparison |
Demands | 3.28 (0.60) | <50th percentile |
Control | 3.55 (0.66) | >50th percentile |
Managers' support | 3.79 (0.98) | <50th percentile |
Peer support | 4.13 (0.62) | >50th percentile |
Relationships | 3.93 (0.75) | <50th percentile |
Role | 4.52 (0.47) | >50th percentile |
Change | 3.66 (0.82) | <50th percentile |
Psychosomatic symptom | Mean (SD) | % of scores ≥ 4 |
Palpitations | 2.23 (1.05) | 13.5% |
Sleep disorders | 3.04 (1.18) | 39.8% |
Depression | 2.38 (1.11) | 17.5% |
Irritability | 2.62 (1.04) | 21.9% |
Anxiety | 2.24 (1.14) | 15.8% |
Physical and mental tiredness | 3.17 (1.07) | 43.3% |
Headache | 2.38 (1.17) | 20.3% |
Osteoarticular pain | 2.49 (1.29) | 26.8% |
Project | D | C | MS | PS | RE | RO | C |
Palpitations | -0.21** | -0.02 | -0.03 | -0.05 | -0.24*** | -0.06 | -0.14* |
Sleep disorders | -0.18** | -0.13 | -0.27*** | -0.20** | -0.34*** | -0.16* | -0.24*** |
Depression | -0.36*** | -0.15* | -0.23*** | -0.19** | -0.39*** | -0.19** | -0.27*** |
Irritability | -0.39*** | -0.24*** | -0.24*** | -0.21** | -0.42*** | -0.20** | -0.30*** |
Anxiety | -0.21*** | -0.12 | -0.19** | -0.16* | -0.32*** | -0.29*** | -0.23*** |
Physical and mental tiredness | -0.42*** | -0.26*** | -0.17* | -0.16* | -0.39*** | -0.27*** | -0.26*** |
Headache | -0.09 | -0.05 | -0.06 | -0.16* | -0.14* | -0.09 | -0.12 |
Osteoarticular pain | -0.19** | 0.01 | -0.08 | -0.15* | -0.19** | -0.16* | -0.07 |
Note: D = Demands, C = Control, MS = Managers' support, PS = Peer support, RE = Relationships, RO = Role, C = Change. *p < 0.05, **p < 0.01, ***p < 0.001.
Predictors | Palpitations | Sleep disorders | Depression | Irritability | Anxiety | Physical and mental tiredness | Headache | Osteoarticular pain |
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Age group | 0.67* (0.45–0.98) | 1.34 (1.00–1.78) | 0.87 (0.61–1.25) | 0.76 (0.54–1.06) | 0.87 (0.60–1.27) | 1.16 (0.85–1.56) | 0.85 (0.60–1.20) | 2.35*** (1.56–3.55) |
Gender (female) | 5.45* (1.11–26.66) | 2.18 (0.95–5.02) | 1.74 (0.56–5.34) | 2.31 (0.73–7.31) | 2.34 (0.68–8.09) | 2.63* (1.05–6.59) | 5.65* (1.50–21.20) | 4.16* (1.30–13.31) |
BMI | 2.88* (1.13–7.34 | 1.52 (0.80–2.87) | 1.84 (0.82–4.13) | 0.84 (0.38–1.85) | 2.21 (0.95–5.14) | 2.57* (1.30–5.09) | 3.63** (1.64–8.04) | 2.24* (1.09–4.63) |
Demands | 0.66 (0.24–1.82) | 1.37 (0.69–2.71) | 2.01 (0.87–4.64) | 2.51* (1.14–5.52) | 1.36 (0.57–3.26) | 2.65** (1.29–5.42) | 0.75 (0.32–1.75) | 1.92 (0.88–4.17) |
Control | 0.75 (0.18–3.04) | 0.94 (0.37–2.38) | 0.49 (0.13–1.78) | 0.51 (0.16–1.63) | 0.50 (0.13–1.92) | 2.67 (0.91–7.79) | 0.81 (0.25–2.63) | 1.35 (0.45–4.00) |
Managers' support | 1.57 (0.49–5.05) | 2.02 (0.87–4.71) | 2.74* (1.01–7.44) | 1.55 (0.57–4.17) | 1.68 (0.58–4.83) | 0.52 (0.19–1.42) | 1.19 (0.43–3.33) | 0.62 (0.22–1.76) |
Peer support | 0.48 (0.05–4.93) | 2.07 (0.42–10.12) | 1.24 (0.24–6.43) | 0.29 (0.04–1.86) | 0.50 (0.08–3.16) | 0.61 (0.10–3.60) | 1.23 (0.24–6.26) | 0.35 (0.06–2.22) |
Relationships | 3.89* (1.10–13.79) | 1.58 (0.59–4.27) | 1.36 (0.43–4.29) | 4.85** (1.66–14.19) | 3.38* (1.11–10.29) | 7.09** (1.93–26.03) | 3.04* (1.02–9.08) | 1.53 (0.49–4.80) |
Role | 0.49 (0.09–2.60) | 0.74 (0.26–2.06) | 2.09 (0.70–6.31) | 2.62 (0.88–7.78) | 2.72 (0.89–8.31) | 2.22 (0.68–7.31) | 1.06 (0.33–3.42) | 1.54 (0.50–4.68) |
Change | 0.70 (0.20–2.45) | 1.36 (0.80–2.87) | 0.83 (0.29–2.39) | 0.91 (0.34–2.47) | 1.02 (0.34–3.02) | 1.33 (0.51–3.49) | 1.04 (0.37–2.92) | 0.92 (0.32–2.67) |
Age group | Gender |
|
M | F | |
<30 | 3 | 22 |
30–40 | 7 | 40 |
41–50 | 14 | 34 |
51–60 | 15 | 74 |
61–65 | 1 | 3 |
Total | 40 | 173 |
HSE-MS IT scale | Mean (SD) | Benchmark comparison |
Demands | 3.28 (0.60) | <50th percentile |
Control | 3.55 (0.66) | >50th percentile |
Managers' support | 3.79 (0.98) | <50th percentile |
Peer support | 4.13 (0.62) | >50th percentile |
Relationships | 3.93 (0.75) | <50th percentile |
Role | 4.52 (0.47) | >50th percentile |
Change | 3.66 (0.82) | <50th percentile |
Psychosomatic symptom | Mean (SD) | % of scores ≥ 4 |
Palpitations | 2.23 (1.05) | 13.5% |
Sleep disorders | 3.04 (1.18) | 39.8% |
Depression | 2.38 (1.11) | 17.5% |
Irritability | 2.62 (1.04) | 21.9% |
Anxiety | 2.24 (1.14) | 15.8% |
Physical and mental tiredness | 3.17 (1.07) | 43.3% |
Headache | 2.38 (1.17) | 20.3% |
Osteoarticular pain | 2.49 (1.29) | 26.8% |
Project | D | C | MS | PS | RE | RO | C |
Palpitations | -0.21** | -0.02 | -0.03 | -0.05 | -0.24*** | -0.06 | -0.14* |
Sleep disorders | -0.18** | -0.13 | -0.27*** | -0.20** | -0.34*** | -0.16* | -0.24*** |
Depression | -0.36*** | -0.15* | -0.23*** | -0.19** | -0.39*** | -0.19** | -0.27*** |
Irritability | -0.39*** | -0.24*** | -0.24*** | -0.21** | -0.42*** | -0.20** | -0.30*** |
Anxiety | -0.21*** | -0.12 | -0.19** | -0.16* | -0.32*** | -0.29*** | -0.23*** |
Physical and mental tiredness | -0.42*** | -0.26*** | -0.17* | -0.16* | -0.39*** | -0.27*** | -0.26*** |
Headache | -0.09 | -0.05 | -0.06 | -0.16* | -0.14* | -0.09 | -0.12 |
Osteoarticular pain | -0.19** | 0.01 | -0.08 | -0.15* | -0.19** | -0.16* | -0.07 |
Predictors | Palpitations | Sleep disorders | Depression | Irritability | Anxiety | Physical and mental tiredness | Headache | Osteoarticular pain |
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
Age group | 0.67* (0.45–0.98) | 1.34 (1.00–1.78) | 0.87 (0.61–1.25) | 0.76 (0.54–1.06) | 0.87 (0.60–1.27) | 1.16 (0.85–1.56) | 0.85 (0.60–1.20) | 2.35*** (1.56–3.55) |
Gender (female) | 5.45* (1.11–26.66) | 2.18 (0.95–5.02) | 1.74 (0.56–5.34) | 2.31 (0.73–7.31) | 2.34 (0.68–8.09) | 2.63* (1.05–6.59) | 5.65* (1.50–21.20) | 4.16* (1.30–13.31) |
BMI | 2.88* (1.13–7.34 | 1.52 (0.80–2.87) | 1.84 (0.82–4.13) | 0.84 (0.38–1.85) | 2.21 (0.95–5.14) | 2.57* (1.30–5.09) | 3.63** (1.64–8.04) | 2.24* (1.09–4.63) |
Demands | 0.66 (0.24–1.82) | 1.37 (0.69–2.71) | 2.01 (0.87–4.64) | 2.51* (1.14–5.52) | 1.36 (0.57–3.26) | 2.65** (1.29–5.42) | 0.75 (0.32–1.75) | 1.92 (0.88–4.17) |
Control | 0.75 (0.18–3.04) | 0.94 (0.37–2.38) | 0.49 (0.13–1.78) | 0.51 (0.16–1.63) | 0.50 (0.13–1.92) | 2.67 (0.91–7.79) | 0.81 (0.25–2.63) | 1.35 (0.45–4.00) |
Managers' support | 1.57 (0.49–5.05) | 2.02 (0.87–4.71) | 2.74* (1.01–7.44) | 1.55 (0.57–4.17) | 1.68 (0.58–4.83) | 0.52 (0.19–1.42) | 1.19 (0.43–3.33) | 0.62 (0.22–1.76) |
Peer support | 0.48 (0.05–4.93) | 2.07 (0.42–10.12) | 1.24 (0.24–6.43) | 0.29 (0.04–1.86) | 0.50 (0.08–3.16) | 0.61 (0.10–3.60) | 1.23 (0.24–6.26) | 0.35 (0.06–2.22) |
Relationships | 3.89* (1.10–13.79) | 1.58 (0.59–4.27) | 1.36 (0.43–4.29) | 4.85** (1.66–14.19) | 3.38* (1.11–10.29) | 7.09** (1.93–26.03) | 3.04* (1.02–9.08) | 1.53 (0.49–4.80) |
Role | 0.49 (0.09–2.60) | 0.74 (0.26–2.06) | 2.09 (0.70–6.31) | 2.62 (0.88–7.78) | 2.72 (0.89–8.31) | 2.22 (0.68–7.31) | 1.06 (0.33–3.42) | 1.54 (0.50–4.68) |
Change | 0.70 (0.20–2.45) | 1.36 (0.80–2.87) | 0.83 (0.29–2.39) | 0.91 (0.34–2.47) | 1.02 (0.34–3.02) | 1.33 (0.51–3.49) | 1.04 (0.37–2.92) | 0.92 (0.32–2.67) |