Achieving Excellence: The Role of Leadership Styles in Fostering Work Performance and Autonomy in Nurses’ decision-making

Achieving Excellence: The Role of Leadership Styles in Fostering Work Performance and Autonomy in Nurses’ decision-making | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Achieving Excellence: The Role of Leadership Styles in Fostering Work Performance and Autonomy in Nurses’ decision-making Fadiyah Jadid Alanazi, Fathia Ahmed Mersal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4427158/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Leadership is essential to nursing because it influences autonomy in nurses’ decision-making and work performance. Efficient leadership styles have been demonstrated to significantly influence patient outcomes, nursing practice, and organizational success. On the contrary, inadequate leadership can impede nurses' autonomy in decision-making, limit their job satisfaction, and ultimately hinder the quality of patient care. Study Aim This study aims to evaluate the role of leadership styles in fostering work performance and autonomy in nurses’ decision-making at Northern Region Hospitals. Methodology A descriptive exploratory design was used in this study with a sample size of 102 participants. Results The results showed a linear relationship between Transformational leadership style and subjective measures of leader work performance and autonomy in nurses' decision-making. The study results indicated that most staff nurses defined their leader's style as transformational leadership, followed by transactional leadership and then laissez-faire leadership. Conclusion In conclusion, Leadership must enhance and foster autonomy in nurses' decision-making ability. Leaders must improve their leadership style to positively impact autonomy in nurses’ decision-making and improve patients' healthcare. All managers down to the lowest channel must exercise a key leadership role to influence nurses to become good and independent leaders. This study involves nursing practice and nursing management. Nurse managers should receive training on the different leadership philosophies and how to apply the style that best advances the organization’s objectives. Figures Figure 1 Introduction The Healthcare system is an essential work environment that impacts the patients' and society's healthcare. With the dramatic population increase, professional nurse retention is necessary. One of the main principles of a sustainable Vision 2030 that Prince Mohammed bin Salman announced in 2016 was to support and improve the healthcare sectors [1]. Leaders and managers have significant roles in the healthcare system at the professional and environmental levels. Leadership plays a vital role in nursing, shaping nurses' work performance, autonomy, and decision-making abilities [2 ,3]. The nurse manager's leadership style significantly impacts the professional work environment and nurses' satisfaction. The nature of the healthcare environment requires nurses to have the ability to make decisions to respond to clients' needs. As a result, nurses require greater autonomy and participation in decision-making, which results in creativity and promoting critical thinking. A crucial determinant of the sustainability of nursing leadership is the standard of nursing work performance [2]. As a result, nurse leaders must implement various leadership strategies and behaviors that enhance nursing performance, particularly those that promote nurses' willingness to perform better. Motivational leadership techniques impact an organization's goals and practices [4].. The efficacy of nurse leaders may be adversely affected by their heavy workloads, which can provide obstacles and difficulties in achieving optimal nursing performance and, eventually, delivering high-quality care. Therefore, managing the workload of nurse leaders is essential to improving overall nursing performance and the capability to influence and motivate nurses [5]. Autonomy in nurses' decision-making is vital to professional nursing care. Nurse decision-making is the most important feature that affects the quality of care needed for everyday tasks [3]. Nurse managers' roles are increasingly important in today's complex and continually changing healthcare organizations. Thus, the function of their practice has evolved, increasing their accountability, authority, and responsibility for unit management, patient care, and staff development in all aspects of their professional lives. A critical element of the professional role of nurses is the belief in autonomous nursing practice that can be expressed as more significant participation in clinical decision-making. Leadership style can be defined as the ability to persuade others to help them accomplish a goal. Leadership and its styles have significant impacts on healthcare organizations and patient safety [6]. These styles affect everyone, from seniors to the newest manager. Furthermore, leadership style may affect the decision-making style and skills of the nurse manager, which is a crucial feature of the nurse role in healthcare organizations [7]. Decision-making is described in the literature interchangeably and uses several terms, such as clinical reasoning, clinical judgment, and decision-making. Autonomy in clinical decision-making is a process that requires nurses to be more knowledgeable, work within a supportive environment, and access appropriate information sources. Leadership is present at every level of an organization, and its purpose is to enhance client care and services by advocating for professional practice. Staff engagement in decision-making, the organization's philosophy, and leaders' leadership style are indicators of effective leadership. Effective leadership styles have been shown to influence nurses' work performance positively. When nurse leaders exhibit strong leadership qualities, such as clear communication, vision, and support, it fosters a positive work environment that promotes collaboration, motivation, and engagement among nurses [2]. Studies have shown that the leadership style employed can significantly impact autonomy in nurses’ decision-making processes and overall work performance, leading to more effective nursing care and improved healthcare outcomes. The main results in these studies were a correlation between leadership style and job satisfaction, performance, and commitment among healthcare staff working in healthcare settings [2, 8, 9]. Background Several research studies investigate the impact of managers' leadership styles and its impact on nurses, patients, and organizations. It influences employee engagement and job satisfaction, patient outcomes, organizational performance, leadership skills development, and nursing staff integration. Performing effective leadership styles and practices, nursing managers can create an empowering and supporting environment that enhances patient care, promotes professional growth, employee satisfaction, and retention, and drives organizational success [ 2 , 4 , 6 , 10 , 11 ]. Managers' leadership styles play a significant role in the involvement and commitment of nurses, making effective decisions, and improving nurses' autonomy and quality of nursing performance for the organization's benefit. The relationship between managers or leaders and nursing staff had been tense and caused a communication barrier; therefore, nurses could not express their concerns regarding the care decisions needed for patients [ 12 ].. Leadership aims to influence and encourage followers to change [ 10 ]. The director, manager, and nursing leader are responsible for providing a work environment in which the nurse feels supported and motivated to provide high-quality work [ 13 ]. Providing high-quality nursing care to patients requires a compelling and intensely managed leadership style. To have an effective leadership style that motivates others, the manager must build the follower's trust [ 2 ]. Individuals, teams, and organizations engaged in decision-making must determine how to use their limited resources best to achieve their goals. Although nurses are straining to provide high-quality health care to patients, the nurses' limitation of participation in clinical decision-making is ineffective and harmful to the safety of patients [ 3 , 12 ]. Limited participation in autonomy in decision-making has been shown to have negative impact on staff job satisfaction and work performance in terms of mistrust and resentment in hospital management [ 14 ]. As a result, the tension among nurses increased, morale among the staff declined, and job satisfaction and organizational commitment decreased. Nurses need to be influential decision-makers and have autonomy to meet their patients' demands in a healthcare system that is constantly changing. Healthcare organizations face unpredictability due to shifts in patient demands, medical advances, and available resources, necessitating a rethinking of their organizational structure and treatment methods [ 15 ]. Limited research studies have been found focusing on the influence of managers' leadership style on fostering work performance and autonomy in nurses' clinical decisions making. Therefore, this study aims to evaluate the role of leadership styles in fostering work performance and autonomy in nurses’ decision-making in the Hospitals. Study Aim and Objectives This study aims to evaluate the role of leadership styles in fostering work performance and autonomy in nurses’ decision-making in the Hospitals. The main objectives were to investigate the impact of leadership style on autonomy in nurses’ decision-making and to identify the impact of leadership style on nurse’s work performance. Research Hypothesis ​The following null hypotheses were tested at α of .05 level of significance: Ho1 ​ There is no relationship between leadership style and autonomy in autonomy in nurses’ decision-making and work performance. Ha ,​ there is a relationship between leadership style and autonomy in nurses’ decision-making and work performance. Significance of the Study Nursing managers and leaders could impact autonomy in nurses’ decision-making and work performance. Leadership style dramatically impacts how the nursing staff performs at work. Transformational leadership, for example, most often positively impacts work performance through inspiration, motivation, and personalized support. While authoritarian and laissez-faire leadership styles may be unfavorable to productivity and satisfaction among staff members, transactional leadership has beneficial uses. Establishing strong leadership practices is essential for organizations because it empowers workers, creates a pleasant workplace atmosphere, and encourages ongoing advancement and improvement, all of which contribute to improved job performance and overall success [ 16 ]. Methodology Design The research design used a quantitative analytical exploratory research design. A cross-sectional survey was used to collect the data. Convenient sampling was used to recruit study participants. The sample size calculation used G*Power 3.1, an open-source power analysis software application. The estimated sample size should be 81 participants with an actual power of .90 and an effect size of .3. Population and Sampling The survey was sent to nurses via online social groups. The study population included staff nurses who work in a hospital and have more than three years of experience. This will make them more experienced and help them absorb the administrative style followed by their manager. Convenience sampling was used to recruit102 participants. Participants received a consent form, which included details about the study's purpose and the nature of participation. To ensure participants had sufficient time to review the consent form, the form was provided to them approximately (two) days before completing the survey. The exclusion criteria were nurses with less than three years of experience. Data Collection Instruments and Procedure After identifying the eligible participants, the researchers administered structured questionnaires with four part to gather the data. The first part detailed participants' demographic data, including their age, sex, region, educational level, years of experience, level of education, and working department. The second part was about principals’ leadership styles determined by their staff nurses as measured by Multi-Factor Leadership Questionnaire − Adapted Version questionnaires [ 17 ]. The third part measured nurses’ work performance using the Individual Work Performance Questionnaire (IWPQ) [ 18 ]. The fourth questionnaire, the Autonomy Scale, developed by Blegen et al [ 19 ], was used to assess nurses' autonomy related to patient care decisions. The self-reported tool consists of 21 items, ranging from 1 to 5. Instrument and Reliability. An adapted version of the Multifactor Leadership Questionnaire (MLQ) [ 17 ]. It aimed to assess various leadership styles, from leaders who take a backseat to their followers to those who encourage their followers to take on leadership roles themselves. The Cronbach’s alpha measures were 0.94. Individual Work Performance Questionnaire (IWPQ) which consists of 18 questions in three scales: Task performance (5 items), contextual performance (8 items), and counterproductive work behavior (5 items). The participants are asked to consider how often they exhibited a certain behavior in the past three months on a scale from 1 (seldom) to 5 (always) [ 18 ]. The Autonomy Scale developed by Blegen et al., was shown to have acceptable psychometric properties. The original research calculated Cronbach’s alpha for the subject. The reliability coefficient for the care decisions subscale was 0.78, and for the unit operation subscale was 0.92. The content validity of the full scale was assessed by an expert panel and deemed appropriate [ 19 ]. Ethical Consideration The researchers obtained the Institutional Review Board's (IRB's) approval before starting the study. The informed consent was collected from the participants before the survey distribution. Participants were assured that participation in this study was voluntary, and they could withdraw from the study at any time. The relationship with the researchers will not be harmed by a participant's decision to cease participating or to refuse to answer certain questions. Participants know that their questionnaires and any given samples will be retraced and destroyed if they decide to leave the research. No one has withdrawn from the ongoing research. Results The research finding was that the demographic characteristics of the studied nurses found that more than half of them (57.8%) are between the ages of 26 and 35 years, with an average age of (31.52 ± 7.34) years. The majority of the participants (93.1%) were female, and about half (56.9%) had a bachelor's degree. It was also found that (43.1%) of the nurses had less than or equal to 5 years of practical experience, with an average experience of (7.64 ± 6.11) years. Moreover, nearly three-quarters (73.5%) worked in the intensive care unit, critical care, and emergency care. (Table 1 ) Table 1 Demographic Information Parameter N % Age/year ≤ 25 19 18.6 26–35 59 57.8 > 35 24 23.5 Mean and SD 31.52 ± 7.34 Gender Male 7 6.9 Female 95 93.1 Educational level Diploma 30 29.4 Bachelor 58 56.9 Master 14 13.7 Years of Experience 10 years 36 35.3 Mean and SD 7.64 ± 6.11 Working Department Administration 8 7.8 CCU, ICU, Critical care, and emergency care 75 73.5 Internal Nursing 2 2.0 Public health nursing 9 8.8 Outpatient 4 3.9 PHC 4 3.9 OR 8 7.8 Figure 1 describes the leadership styles of nursing managers and leader effectiveness. It shows that half (50%) of the nursing managers often and frequently follow transformational leadership, with a total mean of 2.39 ± 1.040 followed by transactional leadership (43.2%), with a total mean of 2.21 ± 1.040. Meanwhile, less than a quarter of the nurses’ managers (24.5%) follow laissez-faire leadership, with a total mean of 1.62 ± 1.106. Regarding the leader effectiveness of nursing managers among studied nurses, it shows that less than half (40.1%) of their nursing managers lead effectively. The finding shows nurses' autonomy regarding patient care decisions autonomy of the studied nurses. It reveals that the lowest mean was for Refusing to carry out physicians’ orders (2.75 ± 1.124), followed by consulting with MD and other professionals (3.12 ± 1.111). Meanwhile, the highest means were deciding the time to administer care (3.94 ± 1.208), followed by serving as a patient advocate (3.84 ± 1.145). Additionally, it shows the total mean of nurses' autonomy regarding patient care decisions autonomy (3.58 ± .90). The mean (x̄) indicates the average score for each autonomy feature, ranging from 1 to 5. A higher mean score signifies a greater amount of autonomy in that specific domain. Nurses appear to have a moderate to high degree of autonomy in most of the areas outlined, based on the mean scores. For instance, tasks such as acting as a patient advocate, scheduling treatment, collaborating on care plans with the patient, and setting the discharge date received higher average ratings (above 3.5). (Table 2) Table 3 reveals that nurses reported high mean scores for task performance indicators, including timely work completion, prioritizing tasks, efficient work execution, and effective time management. The total mean score for task performance is (3.89 ± 0.85). The nurses reported moderate mean scores for contextual performance indicators, including initiating new tasks, taking on challenging tasks, keeping job-related knowledge and skills up-to-date, creative problem-solving, taking on extra responsibilities, seeking new challenges, and actively participating in meetings and consultations. The total mean score for contextual performance is (3.62± 0.89). Nurses reported low mean scores for counterproductive work behavior indicators, such as complaining about minor work-related issues, making problems bigger, focusing on negative aspects instead of positive ones, talking to colleagues, and talking to people outside the organization. The total mean score for counterproductive work behavior is (1.77±0.75). Additionally, the table found a high mean score for total work performance among the studied nurses (3.87±0.66). The findings showed a highly positive significant correlation between the transformational, laissez-faire leadership styles and subjective measures of leader effectiveness with patient care decision autonomy (p ≤ 0.001). However, transactional leadership style is not correlated with clinical decision-making (p > 0.05). Additionally, it shows a highly positive significant correlation between transactional leadership styles with work performance (p ≤ 0.001) while an insignificant correlation with transformational leadership style, laissez-faire leadership, and subjective measures of leader effectiveness (p > 0.05). (Table 4) Table (4): Correlation between Transformational, Transactional, and Laissez-faire Leadership Style, Subjective Measures of Leader Effectiveness and Patient Care Decisions Autonomy and Work Performance (N = 102) Leadership Style Patient Care Decisions Autonomy Work Performance Person correlation P value Person correlation P value Transformational leadership style .453 ** .000** .054 .588 Transactional leadership style − .090 .368 .472** 0.000** Laissez-Faire Leadership .290 .003** .154 .123 Subjective Measures of Leader Effectiveness .407 ** .000** .001 .995 **Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Table 5 shows the results of a stepwise linear regression model that inspected the relationship between nurses' autonomy regarding patient care decisions autonomy as the dependent variable and personal characteristics and leadership style as the independent variables of the nurses studied. The model selected transformational leadership style effectiveness as the only significant predictor of nurses' autonomy regarding patient care decisions autonomy p < 0.01, indicating that the transformational leadership style effectiveness had a positive and significant effect on nurses' autonomy regarding patient care decisions autonomy. The table also shows that other variables, such as age, gender, educational level, years of experience, and different leadership styles, were excluded from the model because they did not significantly affect nurses' autonomy regarding patient care decisions autonomy. Table 5 Stepwise linear regression model with autonomy in nurses' decision-making as the dependent variable, personal characteristics, and leadership style as the independent variable Autonomy in nurses' decision-making as the dependent variable Independent variable Beta Coefficient Standard error T values P values (Constant) 0.211 7.225 .000 Transformational 0.453 0.82 5.083 .000 R (0.453), R 2 (0.205), F value (25.841), P value (0.000) Excluded variables Age, Gender, Educational level, Years of Experience, Subjective Measures of Leader Effectiveness, Transactional leadership style, Passive Avoidant leadership style. Regarding work performance Table 6 reveals that the model selected transactional leadership style as the only significant predictor of nurses' work performance p < 0.01, indicating that the transactional leadership style positively and significantly affected nurses' work performance. The table also shows that other variables, such as age, gender, educational level, years of experience, and different leadership styles, were excluded from the model because they did not significantly affect nurses' autonomy regarding patient care decisions autonomy. Table 6 Stepwise linear regression model with Work performance as the dependent variable, personal characteristics, and leadership style as the independent variable Work performance as the dependent variable Independent variable Beta Coefficient Standard error T values P values Constant .151 21.252 0.000 Transactional leadership style .472 .060 5.500 0.000 R (0.472), R 2 (.223), F value (10.354), P value (0.000) Excluded variables Age, Gender, Educational level, Years of Experience, Subjective Measures of Leader Effectiveness, Transformational leadership style, Passive Avoidant leadership style. Discussion In healthcare settings, effective clinical leadership plays a crucial role in maintaining a high-quality healthcare system that consistently delivers safe and efficient care [ 20 ]. This study investigated the effect of the role of leadership styles in fostering work performance and autonomy in nurses' decision-making. The study revealed that 57.8% of nurses are aged between 26 and 35, with an average age of 31.52 ± 7.34. Most are female, with 56.9% having a bachelor's degree. 43.1% have less than or equal to 5 years of practical experience, with an average of 7.64 ± 6.11 years. Additionally, our findings showed that nearly half of the nursing managers often follow transformational leadership, followed by transactional leadership. In contrast, less than a quarter of the nurses’ managers follow laissez-faire leadership. Regarding the leader effectiveness of nursing managers, it shows that less than half of them lead effectively. A previous study supported these findings by demonstrating that head nurses obtained the highest scores in transformational leadership, followed by transactional leadership and passive-avoidant leadership [ 21 ]. Similarly, Mohamed [ 15 ] conducted a study on critical care nurses. He found that staff nurses perceived their leaders to have a democratic leadership style as the highest, followed by authoritarian leadership, and finally, laissez-faire leadership. In addition, a study emphasized that transformational leaders invest their time in educating and coaching nurses, focusing on identifying and nurturing their talents, providing professional and personal development guidance, treating subordinates as individuals, and attentively listening to their concerns and uncertainties [ 22 ]. This approach enhances nurses' efficiency and dedication toward achieving established goals. The study found a strong correlation between transformational and laissez-faire leadership styles and patient care decision autonomy, while transactional leadership styles showed a positive correlation with work performance. However, there was no significant correlation with clinical decision-making or leader effectiveness. These findings are further supported by Thabet et al. who discovered a significant correlation between supportive leadership styles and nurses' autonomy regarding patient care decisions autonomy [ 7 ]. They found a positive correlation between directive style (p = 0.006) and analytical style (p = 0.007), while team administration style exhibited a negative correlation with conceptual style (p = 0.001) and behavioral style (p = 0.041). Similarly, a recent study by Ariani et al., confirmed that leadership style significantly positively impacted job satisfaction and nurse performance in Dumi Public Health [ 23 ]. Transformational, transactional, and democratic leadership styles all positively and significantly influenced nurses' performance, while transformational, transactional, laissez-faire and democratic leadership styles positively and significantly impacted job satisfaction. Furthermore, the current study aligns with the findings of Zarina et al. [ 24 ], who reported a positive correlation between leadership style and quality of work life (QWL) among nurses. Their results indicated moderate correlations between the variables, with transactional leadership style showing the highest correlation (r = 0.688, p = 0.000), followed by transformational leadership style (r = 0.617, p = 0.000), democratic leadership style (r = 0.600, p = 0.000), and autocratic leadership style (r = 0.487, p = 0.000). It was further suggested that implementing a transactional leadership style could enhance the quality of life for nurses in their workplace. In contrast, Mohamed reached different conclusions [ 15 ]. They found that the dominant leadership styles among head nurses were democratic, authoritarian, and laissez-faire. Their findings also indicated high agreement among staff nurses regarding their perception of clinical decision-making autonomy. Furthermore, their study revealed no statistically significant relationship between nurses' clinical decision-making and overall leadership styles. Similarly, no statistically significant relationship was observed between nurses' clinical decision-making autonomy and leadership styles. This finding demonstrated that transformational leadership style was the only significant predictor of nurses' autonomy, with other variables like age, gender, education level, years of experience, and different leadership styles excluded as they did not significantly affect nurses' autonomy. Contrary to the previous statement, a study found significant correlations between the leadership orientations of nurses and various factors such as age, sex, employing institution, years of experience, managerial position, and satisfaction with the current unit of employment (p < 0.05) [ 25 ]. They also discovered a significant correlation between nurses' clinical decision-making skills and age, sex, and occupational status (p < 0.05). Additionally, they identified a significant correlation between the mean scores of the Leadership Orientation Scale and the Clinical Decision-Making in Nursing Scale (p < 0.05). The study found that transactional leadership style was the only significant predictor of nurses' work performance, with other variables like age, gender, education, years of experience, and leadership styles excluded as they did not significantly impact nurses' autonomy regarding patient care decisions. A recent study conducted by AlFlayyeh & Alghamdi demonstrated that the effects of four alternative leadership styles, transformational, transactional, authoritative, and laissez-faire, on worker performance were examined using linear regression [ 16 ]. Their results showed a significant positive relationship (β = .262) (p < .01) between transformational leadership and employee performance, β = .176 (p < .05) between transactional leadership and employee performance, and β = .305 (p < .01) between authoritative leadership and employee performance. Nonetheless, no significant correlation was found between the laissez-faire leadership style and the performance of employees (β = .041) (p = 378). The study's overall conclusions corroborate the notion that transformational, transactional, and authoritative leadership positively impacts employees' performance in healthcare sector. Research Limitation The research limitation was that most of the responses were from one region, which caused a generalizability limitation. The methodology of the research uses a cross-sectional design and a self-registered survey. Conclusion The results of this study concluded that a transformational leadership style significantly impacts nurses' autonomy regarding patient care decisions autonomy, whereas a transactional leadership style positively impacts work performance. Therefore, hospitals and nurse administration need to improve the autonomy in nurses’ decision-making to improve work performance and patients' health care. Implication for clinical practice The study's conclusions point to the need for additional research that includes samples from a variety of nationalities if we are to fully comprehend the varied lifestyles of nurse managers in hospitals. It also highlights the need to address the cultural implications of gendered roles and responsibilities and give nurses equal opportunity to participate in shared decision-making and access high-quality healthcare. Healthcare practitioners should get education regarding tailored approaches that address distinct decision-making difficulties. Implications for nursing administration The results of the current study have several consequences for nurse management, hospital administrators, training, and research. When choosing a leadership style, administrators must consider nursing and patient happiness. This style must also align with the institution's beliefs and objectives. Nurse managers should also receive training on the different leadership philosophies and how to apply the style that best advances the organization’s objectives. To enhance the educational opportunities for nursing students, the nursing curriculum should integrate clinical leadership practice and a variety of leadership philosophies. In the clinical setting, nurses should become familiar with various leadership philosophies, as well as the benefits and drawbacks of each, and how each is likely to affect the satisfaction of both patients and nurses. More research is required to determine how culture affects nurse managers' use of particular leadership philosophies. Nursing managers should often ask for feedback on their leadership style and make any required modifications. Hospital administrators should offer additional assistance to nursing managers through training in successful leadership, enculturation, and team building to create a common vision for implementing optimal leadership. Recommendations An in-depth qualitative investigation that provides a comprehensive view of nurses' performance may be helpful to ongoing quantitative studies on nurse performance. It could be interesting and useful to conduct studies that examine how nurses view their role in performance. Transformational leadership training is the best for supervisors because it is more successful than other forms. Head nurses should take classes on transformational leadership and performance evaluation. Programs that instruct head nurses in leadership are necessary. Head nurses must seek feedback on their management style and follow up frequently to see how the nurses are performing. Declarations Ethical Approval: The study approved from the Local committee of bioethics (HAP-09-A-043) at Northern Border University . Informed consent to participate in the study was collected from each participant online before they completed the demographic data. For participants protection, the study did not expose the faculty to any diagnosis or treatment. This means that the research activities did not pose any direct or indirect harm to the participants' health or well-being. The study adhered to the principles outlined in the Helsinki Declaration, which is a set of ethical principles for medical research involving human subjects. This declaration provides guidelines for protecting the rights, safety, and well-being of participants [26]. Conflicts of Interest Statement No conflict of interest has been declared by the authors. Funding Information This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request. Consent for publication availability Not applicable Acknowledgments The authors extend their appreciation and acknowledgment to the Deanship of Scientific Research at Northern Border University, Arar, Saudi Arabia for support this research work. References Kingdom of Saudi Arabia Vision 2030. (2016). Retrieved from: https://www.vision2030.gov.sa/en/programs/NTP Krizanova, A., & Michulek, J. (2022). Does Corporate Culture and Leadership Influence Employee Work Performance? Evidence from the Slovak Republic. Economic & Managerial Spectrum / Ekonomicko-Manažérske Spektrum , 16 (2), 37–48. https://doi-org.sdl.idm.oclc.org/10.26552/ems.2022.2.37-48 Zainal, N. H., Musa, K. I., Rasudin, N. S., & Mamat, Z. (2023). Multilevel Modeling of Individual and Group Level Influences on Critical Thinking and Clinical Decision-Making Skills among Registered Nurses: A Study Protocol. Healthcare (Switzerland) , 11 (8). https://doi-org.sdl.idm.oclc.org/10.3390/healthcare11081169 Cummings, G. G., Tate, K., Lee, S., Wong, C. A., Paananen, T., Micaroni, S. P., & Chatterjee, G. E. (2018). Leadership styles and outcome patterns for the nursing workforce and work environment: A systematic review. International Journal of Nursing Studies, 85 , 19-60. https://doi.org/10.1016/j.ijnurstu.2018.04.016 Alsadaan, N., Salameh, B., Reshia, F. A. A. E., Alruwaili, R. F., Alruwaili, M., Awad Ali, S. A., ... & Jones, L. K. (2023). Impact of Nurse Leaders Behaviors on Nursing Staff Performance: A Systematic Review of Literature. INQUIRY: The Journal of Health Care Organization, Provision, and Financing , 60 , 00469580231178528. ‏ Virgina S. Palweni, Jacobeth M. Malasela, & Moreoagae B. Randa. (2023). Nurse managers’ leadership styles as an impetus to patient safety in an academic hospital. Health SA Gesondheid: Journal of Interdisciplinary Health Sciences , 28 (0), e1–e7. https://doi-org.sdl.idm.oclc.org/10.4102/hsag.v28i0.2344 Thabet, M., Eman, E., Abood, S. A., & Morsy, S. R. (2017). The effect of problem-based learning on nursing students' decision-making skills and styles. Journal of Nursing Education and Practice, 7( 6), 108. Alshahrani, F. M., & Baig, L. A. (2016). Effect of Leadership Styles on Job Satisfaction Among Critical Care Nurses in Aseer, Saudi Arabia. Journal of the College of Physicians and Surgeons--Pakistan : JCPSP, 26 (5), 366–370. https://doi.org/2316 Morsiani, G., Bagnasco, A., & Sasso, L. (2017). How staff nurses perceive the impact of nurse managers’ leadership style in terms of job satisfaction: a mixed method study. Journal of nursing management, 25(2), 119-128. ‏ doi: 10.1111/jonm.12448. Epub 2016 Dec 5. Barr, J., & Dowding, L. (2022). Leadership in Health Care. Sage Major, D. (2019). Developing effective nurse leadership skills. Nursing Standard (Royal College of Nursing (Great Britain) : 1987) , 34 (6), 61–66. https://doi.org/10.7748/ns.2019.e11247 Asiri, S. A., Rohrer, W. W., Al-Surimi, K., Da'ar, O. O., & Ahmed, A. (2016). The association of leadership styles and empowerment with nurses' organizational commitment in an acute health care setting: A cross-sectional study. BMC Nursing, 15 , 38. https://doi.org/10.1186/s12912-016-0161-7 Abualrub, R. F., & Alghamdi, M. G. (2012). The impact of leadership styles on nurses’ satisfaction and intention to stay among Saudi nurses. Journal of Nursing Management, 20 (5), 668-678. https://doi.org/10.1111/j.1365-2834.2011.01320.x Samuel, H., Sehar, S., Afzal, M., & Gilani, S. A. (2018). Influence of supportive leadership on nursing clinical decision making in critical care units at tertiary care hospital Lahore. International Journal of Nursing , 5 (2), 45-71. https://doi.org/DOI: 10.15640/ijn.v5n2a5 Mohamed, N. T. (2018). Relationship between leadership styles and clinical decision-making autonomy among critical care nurses. Egyptian Nursing Journal , 15 (2), 102. AlFlayyeh, S., & Alghamdi, A. B. M. (2023). Leadership Styles and its Impact on Employee Performance: An empirical investigation of Riyadh Private Hospitals. Journal of Population Therapeutics and Clinical Pharmacology , 30 (15), 19-33. ‏ Rowold, J. (2005). Multifactor leadership questionnaire. Psychometric properties of the German translation by Jens Rowold. Redwood City: Mind Garden . Arkadiusz Mirosław Jasiński, Romuald Derbis, & Linda Koopmans. (2023). Polish adaptation and validation of the Individual Work Performance Questionnaire (IWPQ). Medycyna Pracy , 74 (5), 389–398. https://doi-org.sdl.idm.oclc.org/10.13075/mp.5893.01419 Blegen, M. A., Goode, C. J., Johnson, M., Maas, M., Chen, L., & Moorhead, S. (1993). Preferences for decision-making. Journal of Nursing Scholarship, 25, 339-344. Xu, J. H. (2017). Leadership theory in clinical practice. Chinese Nursing Research, 4 (4), 155-157. ‏ Abdelhafiz, I. M., Mah'd Alloubani, A., Klaledeh, M., Mutari, N., & Almukhtar, M. M. (2015). Impact of leadership styles among head nurses on level of job satisfaction among staff nurses. European Scientific Journal . ‏ 203-216 chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://core.ac.uk/download/pdf/328025196.pdf Styron Jr, R. A., & Styron, J. L. (Eds.). (2017). Comprehensive problem-solving and skill development for next-generation leaders. IGI Global. ‏ Ariani, N., Sansuwito, T. B., Prasath, R., Novera, M., Sarli, D., & Poddar, S. (2022). The effect of leadership styles on nurse performances and job satisfaction among nurses in Dumai public hospital: technological innovation as mediator. Malaysian Journal of Medicine & Health Sciences, 18 . ‏ 229- Zarina Begum Ebrahim, Siti Aqilah Hafidzuddin, Muna Kameelah Sauid, Nurul Ain Mustakim, & Noorzalyla Mokhtar. (2022). Leadership Style and Quality of Work Life among Nurses in Malaysia during the COVID-19 Pandemic Crisis. Proceedings , 82 (99), 99. https://doi.org/10.3390/proceedings2022082099 Gürsoy, E., Yeşildere Sağlam, H., Başaran, F., Çetin Atay, E., & Şimal Yavuz, N. (2023). Turkish nurses' leadership orientations and clinical decision-making skills. Leadership in Health Services, 36 (3), 402-417. ‏ https://doi.org/10.1108/LHS-08-2022-0090 World Medical Association. Declaration of Helsinki- Ethical Principles for Medical Research Involving Human Subjects 2015. Available from: http://www.wma.net/en/30publications/10policies/b3 (accessed 14 May 2023) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4427158","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309045589,"identity":"598a72f9-80dd-4634-9b92-b26bfecf342c","order_by":0,"name":"Fadiyah Jadid Alanazi","email":"data:image/png;base64,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","orcid":"","institution":"Northern Border University","correspondingAuthor":true,"prefix":"","firstName":"Fadiyah","middleName":"Jadid","lastName":"Alanazi","suffix":""},{"id":309045591,"identity":"c0e745cb-39ce-42a2-ac6c-bd3cdfac294a","order_by":1,"name":"Fathia Ahmed Mersal","email":"","orcid":"","institution":"Northern Border University","correspondingAuthor":false,"prefix":"","firstName":"Fathia","middleName":"Ahmed","lastName":"Mersal","suffix":""}],"badges":[],"createdAt":"2024-05-15 20:09:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4427158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4427158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57948207,"identity":"5e9a1dd7-4761-4a05-879a-3e8bd83a987b","added_by":"auto","created_at":"2024-06-07 20:31:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of nurses regarding Leadership Styles and Subjective Measures of Leader Effectiveness (N=102).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4427158/v1/c5ad118247b2f6a00f2387e2.jpg"},{"id":66302519,"identity":"266040a6-cda8-432c-99ea-c44bd118b1e4","added_by":"auto","created_at":"2024-10-10 06:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":874024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4427158/v1/51b8089d-6b0c-491b-b323-989772fe248d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Achieving Excellence: The Role of Leadership Styles in Fostering Work Performance and Autonomy in Nurses’ decision-making","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Healthcare system is an essential work environment that impacts the patients\u0026apos; and society\u0026apos;s healthcare. With the dramatic population increase, professional nurse retention is necessary. One of the main principles of a sustainable Vision 2030 that Prince Mohammed bin Salman announced in 2016 was to support and improve the healthcare sectors [1]. Leaders and managers have significant roles in the healthcare system at the professional and environmental levels. Leadership plays a vital role in nursing, shaping nurses\u0026apos; work performance, autonomy, and decision-making abilities [2 ,3]. The nurse manager\u0026apos;s leadership style significantly impacts the professional work environment and nurses\u0026apos; satisfaction. The nature of the healthcare environment requires nurses to have the ability to make decisions to respond to clients\u0026apos; needs. As a result, nurses require greater autonomy and participation in decision-making, which results in creativity and promoting critical thinking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA crucial determinant of the sustainability of nursing leadership is the standard of nursing work performance [2]. As a result, nurse leaders must implement various\u0026nbsp;leadership strategies and behaviors that enhance nursing performance, particularly those that promote nurses\u0026apos; willingness to perform better. Motivational leadership techniques impact an organization\u0026apos;s goals and practices [4].. The efficacy of nurse leaders may be adversely affected by their heavy workloads, which can provide obstacles and difficulties in achieving optimal nursing performance and, eventually, delivering high-quality care. Therefore, managing the workload of nurse leaders is essential to improving overall nursing performance and the capability to influence and motivate nurses [5].\u003c/p\u003e\n\u003cp\u003eAutonomy in nurses\u0026apos; decision-making is vital to professional nursing care. Nurse decision-making is the most important feature that affects the quality of care needed for everyday tasks [3]. Nurse managers\u0026apos; roles are increasingly important in today\u0026apos;s complex and continually changing healthcare organizations. Thus, the function of their practice has evolved, increasing their accountability, authority, and responsibility for unit management, patient care, and staff development in all aspects of their professional lives. A critical element of the professional role of nurses is the belief in autonomous nursing practice that can be expressed as more significant participation in clinical decision-making.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeadership style can be defined as the ability to persuade others to help them accomplish a goal. Leadership and its styles have significant impacts on healthcare organizations and patient safety [6]. These styles affect everyone, from seniors to the newest manager. Furthermore, leadership style may affect the decision-making style and skills of the nurse manager, which is a crucial feature of the nurse role in healthcare organizations [7]. Decision-making is described in the literature interchangeably and uses several terms, such as clinical reasoning, clinical judgment, and decision-making. Autonomy in clinical decision-making is a process that requires nurses to be more knowledgeable, work within a supportive environment, and access appropriate information sources.\u003c/p\u003e\n\u003cp\u003eLeadership is present at every level of an organization, and its purpose is to enhance client care and services by advocating for professional practice. Staff engagement in decision-making, the organization\u0026apos;s philosophy, and leaders\u0026apos; leadership style are indicators of effective leadership. Effective leadership styles have been shown to influence nurses\u0026apos; work performance positively. When nurse leaders exhibit strong leadership qualities, such as clear communication, vision, and support, it fosters a positive work environment that promotes collaboration, motivation, and engagement among nurses [2]. Studies have shown that the leadership style employed can significantly impact autonomy in nurses\u0026rsquo; decision-making processes and overall work performance, leading to more effective nursing care and improved healthcare outcomes. The main results in these studies were a correlation between leadership style and job satisfaction, performance, and commitment among healthcare staff working in healthcare settings [2, 8, 9].\u003c/p\u003e\n\u003ch3\u003eBackground\u003c/h3\u003e\n\u003cp\u003eSeveral research studies investigate the impact of managers' leadership styles and its impact on nurses, patients, and organizations. It influences employee engagement and job satisfaction, patient outcomes, organizational performance, leadership skills development, and nursing staff integration. Performing effective leadership styles and practices, nursing managers can create an empowering and supporting environment that enhances patient care, promotes professional growth, employee satisfaction, and retention, and drives organizational success [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Managers' leadership styles play a significant role in the involvement and commitment of nurses, making effective decisions, and improving nurses' autonomy and quality of nursing performance for the organization's benefit.\u003c/p\u003e \u003cp\u003eThe relationship between managers or leaders and nursing staff had been tense and caused a communication barrier; therefore, nurses could not express their concerns regarding the care decisions needed for patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].. Leadership aims to influence and encourage followers to change [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The director, manager, and nursing leader are responsible for providing a work environment in which the nurse feels supported and motivated to provide high-quality work [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Providing high-quality nursing care to patients requires a compelling and intensely managed leadership style. To have an effective leadership style that motivates others, the manager must build the follower's trust [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Individuals, teams, and organizations engaged in decision-making must determine how to use their limited resources best to achieve their goals.\u003c/p\u003e \u003cp\u003eAlthough nurses are straining to provide high-quality health care to patients, the nurses' limitation of participation in clinical decision-making is ineffective and harmful to the safety of patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Limited participation in autonomy in decision-making has been shown to have negative impact on staff job satisfaction and work performance in terms of mistrust and resentment in hospital management [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As a result, the tension among nurses increased, morale among the staff declined, and job satisfaction and organizational commitment decreased.\u003c/p\u003e \u003cp\u003eNurses need to be influential decision-makers and have autonomy to meet their patients' demands in a healthcare system that is constantly changing. Healthcare organizations face unpredictability due to shifts in patient demands, medical advances, and available resources, necessitating a rethinking of their organizational structure and treatment methods [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Limited research studies have been found focusing on the influence of managers' leadership style on fostering work performance and autonomy in nurses' clinical decisions making. Therefore, this study aims to evaluate the role of leadership styles in fostering work performance and autonomy in nurses\u0026rsquo; decision-making in the Hospitals.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy Aim and Objectives\u003c/h2\u003e \u003cp\u003eThis study aims to evaluate the role of leadership styles in fostering work performance and autonomy in nurses\u0026rsquo; decision-making in the Hospitals. The main objectives were to investigate the impact of leadership style on autonomy in nurses\u0026rsquo; decision-making and to identify the impact of leadership style on nurse\u0026rsquo;s work performance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Hypothesis\u003c/h3\u003e\n\u003cp\u003e​The following null hypotheses were tested at α of .05 level of significance:\u003c/p\u003e \u003cp\u003e \u003cb\u003eHo1\u003c/b\u003e​ There is no relationship between leadership style and autonomy in autonomy in nurses\u0026rsquo; decision-making and work performance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHa\u003c/b\u003e,​ there is a relationship between leadership style and autonomy in nurses\u0026rsquo; decision-making and work performance.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSignificance of the Study\u003c/h2\u003e \u003cp\u003eNursing managers and leaders could impact autonomy in nurses\u0026rsquo; decision-making and work performance. Leadership style dramatically impacts how the nursing staff performs at work. Transformational leadership, for example, most often positively impacts work performance through inspiration, motivation, and personalized support. While authoritarian and laissez-faire leadership styles may be unfavorable to productivity and satisfaction among staff members, transactional leadership has beneficial uses. Establishing strong leadership practices is essential for organizations because it empowers workers, creates a pleasant workplace atmosphere, and encourages ongoing advancement and improvement, all of which contribute to improved job performance and overall success [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThe research design used a quantitative analytical exploratory research design. A cross-sectional survey was used to collect the data. Convenient sampling was used to recruit study participants. The sample size calculation used G*Power 3.1, an open-source power analysis software application. The estimated sample size should be 81 participants with an actual power of .90 and an effect size of .3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and Sampling\u003c/h2\u003e \u003cp\u003eThe survey was sent to nurses via online social groups. The study population included staff nurses who work in a hospital and have more than three years of experience. This will make them more experienced and help them absorb the administrative style followed by their manager. Convenience sampling was used to recruit102 participants. Participants received a consent form, which included details about the study's purpose and the nature of participation. To ensure participants had sufficient time to review the consent form, the form was provided to them approximately (two) days before completing the survey. The exclusion criteria were nurses with less than three years of experience.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Instruments and Procedure\u003c/h2\u003e \u003cp\u003eAfter identifying the eligible participants, the researchers administered structured questionnaires with four part to gather the data. The first part detailed participants' demographic data, including their age, sex, region, educational level, years of experience, level of education, and working department. The second part was about principals\u0026rsquo; leadership styles determined by their staff nurses as measured by Multi-Factor Leadership Questionnaire\u0026thinsp;\u0026minus;\u0026thinsp;Adapted Version questionnaires [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The third part measured nurses\u0026rsquo; work performance using the Individual Work Performance Questionnaire (IWPQ) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The fourth questionnaire, the Autonomy Scale, developed by Blegen et al [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], was used to assess nurses' autonomy related to patient care decisions. The self-reported tool consists of 21 items, ranging from 1 to 5.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInstrument and Reliability.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAn adapted version of the Multifactor Leadership Questionnaire (MLQ) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It aimed to assess various leadership styles, from leaders who take a backseat to their followers to those who encourage their followers to take on leadership roles themselves. The Cronbach\u0026rsquo;s alpha measures were 0.94.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividual Work Performance Questionnaire (IWPQ) which consists of 18 questions in three scales: Task performance (5 items), contextual performance (8 items), and counterproductive work behavior (5 items). The participants are asked to consider how often they exhibited a certain behavior in the past three months on a scale from 1 (seldom) to 5 (always) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Autonomy Scale developed by Blegen et al., was shown to have acceptable psychometric properties. The original research calculated Cronbach\u0026rsquo;s alpha for the subject. The reliability coefficient for the care decisions subscale was 0.78, and for the unit operation subscale was 0.92. The content validity of the full scale was assessed by an expert panel and deemed appropriate [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEthical Consideration\u003c/h2\u003e \u003cp\u003e The researchers obtained the Institutional Review Board's (IRB's) approval before starting the study. The informed consent was collected from the participants before the survey distribution. Participants were assured that participation in this study was voluntary, and they could withdraw from the study at any time. The relationship with the researchers will not be harmed by a participant's decision to cease participating or to refuse to answer certain questions. Participants know that their questionnaires and any given samples will be retraced and destroyed if they decide to leave the research. No one has withdrawn from the ongoing research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe research finding was that the demographic characteristics of the studied nurses found that more than half of them (57.8%) are between the ages of 26 and 35 years, with an average age of (31.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34) years. The majority of the participants (93.1%) were female, and about half (56.9%) had a bachelor\u0026apos;s degree. It was also found that (43.1%) of the nurses had less than or equal to 5 years of practical experience, with an average experience of (7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11) years. Moreover, nearly three-quarters (73.5%) worked in the intensive care unit, critical care, and emergency care. (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic Information\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eAge/year\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean and SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eGender\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eEducational level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eYears of Experience\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean and SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWorking Department\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCU, ICU, Critical care, and emergency care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternal Nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic health nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure 1 describes the leadership styles of nursing managers and leader effectiveness. It shows that half (50%) of the nursing managers often and frequently follow transformational leadership, with a total mean of 2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.040 followed by transactional leadership (43.2%), with a total mean of 2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.040. Meanwhile, less than a quarter of the nurses\u0026rsquo; managers (24.5%) follow laissez-faire leadership, with a total mean of 1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.106. Regarding the leader effectiveness of nursing managers among studied nurses, it shows that less than half (40.1%) of their nursing managers lead effectively.\u003c/p\u003e\n\u003cp\u003eThe finding shows nurses\u0026apos; autonomy regarding patient care decisions autonomy of the studied nurses. It reveals that the lowest mean was for Refusing to carry out physicians\u0026rsquo; orders (2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.124), followed by consulting with MD and other professionals (3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.111). Meanwhile, the highest means were deciding the time to administer care (3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.208), followed by serving as a patient advocate (3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.145). Additionally, it shows the total mean of nurses\u0026apos; autonomy regarding patient care decisions autonomy (3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;.90).\u003c/p\u003e\n\u003cp\u003eThe mean (x̄) indicates the average score for each autonomy feature, ranging from 1 to 5. A higher mean score signifies a greater amount of autonomy in that specific domain. Nurses appear to have a moderate to high degree of autonomy in most of the areas outlined, based on the mean scores. For instance, tasks such as acting as a patient advocate, scheduling treatment, collaborating on care plans with the patient, and setting the discharge date received higher average ratings (above 3.5). (Table 2)\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"757\" height=\"735\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 reveals that nurses reported high mean scores for task performance indicators, including timely work completion, prioritizing tasks, efficient work execution, and effective time management. The total mean score for task performance is (3.89 \u0026plusmn; 0.85). The nurses reported moderate mean scores for contextual performance indicators, including initiating new tasks, taking on challenging tasks, keeping job-related knowledge and skills up-to-date, creative problem-solving, taking on extra responsibilities, seeking new challenges, and actively participating in meetings and consultations. The total mean score for contextual performance is (3.62\u0026plusmn; 0.89). Nurses reported low mean scores for counterproductive work behavior indicators, such as complaining about minor work-related issues, making problems bigger, focusing on negative aspects instead of positive ones, talking to colleagues, and talking to people outside the organization. The total mean score for counterproductive work behavior is (1.77\u0026plusmn;0.75). Additionally, the table found a high mean score for total work performance among the studied nurses (3.87\u0026plusmn;0.66).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAl4AAAKFCAYAAAAd2OdqAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAABhaVRYdFNuaXBNZXRhZGF0YQAAAAAAeyJjbGlwUG9pbnRzIjpbeyJ4IjowLCJ5IjowfSx7IngiOjYwNiwieSI6MH0seyJ4Ijo2MDYsInkiOjY0NX0seyJ4IjowLCJ5Ijo2NDV9XX0HlBjSAADOUklEQVR4Xuy9z4tVy7vfvz/f/BOJGDF64M4CIUSIUQeS2AdCZpKodyJJEPrAhRCMZuDgDhykTyRwIR8FSYIj+5wgmQXUBPlc+8SAEAKZXbAPYsSE/BN+16tc733eXb3W/tG99+rdvd8vKFet+vk8VbWqnlW12v27rw2jEEIIIYSwdP6/9hpCCCGEEJZMDK8QQgghhIGI4RVCCCGEMBAxvEIIIYQQBiKGVwghhBDCQMTwCiGEEEIYiGNleP3ud7/rdT/88EObqpvvv/++pONa89NPP43L+fXXX9vQ2fnuu+9Gv/zyS/Ejh8oS7p8FyYqj7BqXF3cQmYeEtpGsP/74Yxu6F8KVBv24X5ZelKu6po2bWfD+wO+47rXTmDkoKruucxYYY11ja1l4O9Rt7v2BXNz3jZNZ6Rpz6qdZ293HZJ3HZfZ4r3fI9u3Dx2btuubCofB5Us/5osek19HlgPrwzzsPzJPHxwSOceVzvMJA4QcZ/z5ew4rD/+N1XNjY2CjX3d1d/u+xPfebm5vF30edp4Zw4kk3D+fPn/+6tbVV/FwpQ04y7ezszF22l0V+R7JS93Fhe3u7yKy2cur2od38HrryHQbVOW3czIr6Cz27IM77Cz9hh9FLOvTVWeN1MYaGHD9qa+qtn0F/hqSTy8o4mFVHpx5zuq+fp0loLPblIU7OWdS4WgS0H/J5u2sOOcz4OyySQc8594sck/SBl13XBQedB+bNo7Hn+tH2hEkWcZCxLjSvhNXmWO14vXz5svXt5dy5c6PHjx+3d8PBGw9vTPfu3Sv3b968YcSPmoe53ItLly6Nmod07req5oEs1+fPn5cr8HaIvieJz58/t75v0Je0l+CN8f79++3dyeD169flil56458XxhXj7caNG21IP4y9jx8/tnffnqUPHz60d8vF9aPe+jluFsPW902n+vm5du1a6zsctBPtRR2LgnGq5/Qod5Dm5fbt2+XqY+KoWfSYZB7pmyv71pJZYRzNs+Yw9hqjqIx17Y5q3Xj16tX4GWGH8sKFC8UfTi4n7huvelu36xhGcZMMId+27SqDB+XJkyfjCQz0MMuQuHXrVrkCfh4wPXQqv6tsobKpR7x48WJPuY7k9e36+qhB9fsWu/x929t9baptca7ye5t6vrdv37ah+9FEw8Qk2TVpUtfly5dLGOVQJm2vcnGS2/tMfm8Lz+fGrFA74LSIeh5kURrwtp13AUM39IX379+Xq7eX2lH3OOIJx+91S38Pw5Ee0IUxhEN+byfhYdLd5fF4LRI1Xr+3u/Sk/q5nDsMFA5R81IlhpEWJMBarmzdvlrwaZ1y9PsnkMvuYq/sRPK3L1dU+k9Bzz/OtvhB1u8kvGXRPOulGOh+LXqbCvH0VhlO503j27Fm5+lzi7el1uixyyOj64JTHy1Eba0xp/Lr84O0EqpP08rtMKsfdrLr30VVPV5v4GASXVXKhd82dO3fKVXMPafRsMK/Dp0+f9hiLrid+oTBk8nHjSC5cWDEay/3YwXYxotdbtNAM5PF2rqdRHqmsbV5t65KOe9Jp+5mr/DV9xxZsPxOO8y1j1a+t/br+GqWryyJcMklPQH7pytXzc6/6tT2uMnDEebvVzNKmlEc4fuFpFSe5alwenMtJ3YQJr6eWW2mpB4efMhSntOon1eNpFYd+HodTv9ftqXr7+pM4lxPqNqn9ktvzUq/CJYvnocxaNsmve5C8QHn4KU95VabagnKVTnFOnQ+/6qvl6ULyyAH1SXa1q8pCHlAbEg5dcZKpLsvj8FNfrSN+xXUhnZQPR/l9bV3LwNXzcf3555/H6QmTLOjjuhFOvMaG7mvUZu7qdEpDuPxcJS/6Eed51b6ADJIDFEcYZXDv+V0m/ODt5O1JvJfvfaRyJo0tkDyqS0yqR2XXbSI/ZULd91wJq1E+HJBW6VUWOgkvS3UQ5nEK40r53obT2iQcHSdux4utapys/Hrruhng5Xrx4sVy5Q2jRm8k7LRot6V+g+nKB+zUNA9J8fOWLvQWo50R3uib9p96TNQ8iOXKGyoySG6HNx3etnHozZVjT6AOZGoeyHJfvxU1D2fvdryYpU3r4xu1l8r2ncEudGymtmN3hLfNrmMhdhhIyxtdM9nsOaoS2jER6E068kC9a6j+oO+1w/jly5dyFc0ENz6u0i7V2bNny1Vvs/Ogdjxz5sz4bV07P/Du3btypY+QnTal3r6jsr6+7hozjsb76dOnx/319OnTchUPHjxofd3ojR1dABnUjrNAWyC/ZOctvk/PPuYZc33trTaXHrT9LCAr4wP8uZ8VnnONrX/4D/9haQfkok8Ym33POHkYG4Rdv3594nzCc+pzk88F6j/C1Qca4wJZQHPfLM8hR+rIT1rtQGqcaS6exLT5SXH1vDYvXfXM0iYOZagPu8Yu5ascxirtqP6iPxmT/qxqV/LUqVPFgcKEjxuXn+dHnzOE1ePEGV7asmYgHhYmKcrpe5D68En4sBMCkynwYLJATpKDiUzy6viD7Wm+kemaFGv60szbpvPqzISjRRP9VE+fcUt65GFi0UQ2ifobsi6QmTKvXLky82J7GKhP7e3fdDCRqg9lPMpIlFHQxzx9fRgW/V2QH6FggNH+tXG/LLra+6CwAC5q7KA/cwgvfn701fWM40cPngUZlH3wfJEWur6do06Vjz60CXXqJRS/2mne5/CwaFxLBzea1RaLoH5+6jY5DHpB46VARpYMUF5uJs3v83D16tWih4+dsDqcOMOLh5GBrIW8Dy1iMmwcFl/QTgCDty5Pb8RCk5DSYTQgR/1Gph0SGTPTJkry68FU3hq9SWGcqR7eePCz68AEO+lNbRqztqmjCURvXX1GlGBSl+y69u3UMEFpop/VyPA2gtoYe/ToUbliBM3ypihjSTuL8xojMjZYQJBN5WmnibbWpElbIjv94Eaac5i+lmFHm6jt593B03OkfqZfZjVCkNsXCGToMgq6UH/CPGOur7015rQ7o/aYFd9x7ELjZRLIwvPLIs/4IE/fM878wVjCGKK9J31LKWRM0Ucah9JbOyqU63OTDA83cOZ9DjWvau49jHFNm0gm3DKY1iYHwdcbjVftzNZjXuHsvGv3fdrJgaCPMRiZMw4rc1gCzaA9VjSDqZxhu3OaiaCENRPL2N9MXuX8u5mYxt8s4Dg3By+TfEBahZGnhvKII6/fy5HfoS7CVafkUH4HGVQO6ZWXOlx+OcLr+oXuXR/qVNvoXv5abuhrU5ezqw0lN87rQ1aHvKT1NN7mrjN5VS/p5a/71uXBgbeR10XdnldtRZq+dgWvw8tzvA1qR5zT1YYCmeowL8vlrPsaJB9l1LqCh6kel131676WHer2EArDqT6HMK/f87pepAHXTzJ1yex98oc//GHsx0Ffe9ey6OrU9TjI7HrW9cgP8uPIB5TtOqqtu8YiZXtalSG8bjlRh3WNi678ipMu6C8/cX3tCi6r2pY0dd2Kw3l5xNXtgKv7wKnTUp6YVA90tYn3IXm8jK7nokb6CunTldfbS/WDwnDq81pWz+v1haPnd/zTdEw4ALwt8vY5yzY3b9O8pS1ySzyEEJaNjvOcZrEvu3BHBbt9vtsJjRF06KPAEIbgxB01DgnHCmyX+zFJFxwdsOVL+hBCOC7wcrm19dt3cHpPP0qjS0dnLtPGxsb44/8QVp3seC0A3r4mfRjJG2OaOYRw3OClUX/ZLXZ2dnrnuqGod+EwDvXRfwirTgyvEEIIIYSByFFjCCGEEMJAxPAKIYQQQhiIGF4hhBBCCAMRwyuEBcBftvLBb+30E0UHhb8qU1l86HwSQS/aiT9Ska76jz2BuIO2gZdZ//UxZfXFKRwnkGPe/1A1hBBqYniFsAD4iyr+2gs2NzfLX7Hyl1b8f0cs/geBP5vnf3Xn/yeCWX766Lihv5rjf5vn/7ij7QC99d8G6GeEaNN5/5qOMtV+DgYU9dJn9BH/3YuMutpYVv/pp3HmNf5CCMGJ4RXCktBPjhz0p1H0kzf8/0QYHSfxP4fE+MEw8v8XSoZS/UPOi0Q/gMyPD6tufiIMo4qfS6K9a4ONdBjTs/5sSwghdBHDK4Qlod+k8x0UHV8pDMNCYezw6FgNP7swgHHiuzGeHrgqjHRc2aXRMZv7OcLz9I7S4CSfysP5caobRH4c2ndE2IWO92qDknvtfNW/X+fy1A75Z6XrtzXZ0WJHTf8flOTiB4cFv7XHDpnaPoQQ5qZ5swshLAB+a41Hyp3/Vh1+3fvvtXFV+u329+I8XL/hpt9eA8Xpd9ooj3t+N4447okjTGUoDeWoHq7g9331kl86kgaUj3CXD79+Pw8/robylEZ4Ov0GHum68s+KZJTMQHmEqf3w17IQV4cBaQ8jTwhhvcmOVwgLplmUy1EVTr/NyQ4Rvy2HY3eGK7/d6TQGQtllIV8XfPfUGALFf+bMmXLVkZlg14YdG/9N0MZw2fNt1N27d1vfb8eZqpd77bTV35Tx6ww1z549K1eO7PhJLMmOrOwMoSt+5KqZdgSreNqKMmBRO16z8OjRo04Z6YNlHYGGEE4+MbxCGBCMoNooWxUwXmBra6tcJ9F1VFeDgSJdD/qd2077BwsCA1Jl1m6e9jx79mzr+w0/1uQo8cqVK8WPsRdDK4SwKGJ4hTAAfJiNIcLujRbxeXdo2EljFwm0U8U3R4tA31vxBwH1Ttwk9P0Tu0NAOeiHgYmsGC3Qpau+I5sEhtYshuC8qN3ev38/7o9bt26VKzLzYT8OY5Rv7Pzjf/TSPfGz6BFCCGOaN8UQwiHRd1FyXd8G8c2Qp+kLA32XJKfvrShXYaQBT8v3UAK/wvVNE87LwCGDl+Fp9U2Xwr1MyeTp/TsqheGoo0ZtJlwulS1cr3modfVyXDfJXbc7zvtS/aW2r+NDCGEa+ZHsEMKRwY5RY8Ts+8vGVYUjSL51O+jRaQghxPAKIRwZHOtxlLdrx3erCkeS58+fL9+dzfsfuYYQgojhFUI4UjBo+LB91XeR+JaLv85cdQMxhLDaxPAKIYQQQhiI/FVjCCGEEMJAxPAKIYQQQhiIGF4hhBBCCAMRwyuEEEIIYSBieIUQQgghDEQMrxBCCCGEgYjhFUIIIYQwEDG8QgghhBAGIoZXCCGEEMJAxPAKIYQQQhiIfT8Z9Lvf/a71hRBCCCGEw+KmVqfh9Rd/8RftXQghhBAWyR/90R+t9Tq7bvqjr5taOWoMIYQQQhiIGF4hhBBCCAMRwyuEEEIIYSBieIUQQgghDEQMrxBCCCGEgYjhFUIIIYQwEDG8QgghhBAGIoZXCCGEEMJAxPAKIYQQQhiIGF4hhBBCCAMRwyuEEEIIYSBieIUQQgghDEQMrxBCCCGEgYjhFUIIIYQwEDG8QgghhBAGIoZXWCv+6I/+aOz+yT/5J6N/9+/+3b4wULju5+F//I//MS4P/yT+3t/7e+O07qj/oPzv//2/x+UcRP4QwsnE5yYc889J5E//9E/HOqIz99A33yp+KGJ4hbXiv/7X/9r6vj2c//Sf/tPR8+fP25DR6N//+39froT/m3/zb8b38/A3/+bfHN28ebO9m8x/+S//ZZwWOf7iL/5idObMmdG//tf/evSf//N/LuHz8o//8T8eXbp0qZT76dOnNjSEsO4wNzHHyDH/nDSYN7e3t8c6Pn78uNxD13z7L/7FvyjxQxqhMbzCWvFX/+pfHT94r169KlcmIwwVkLHDrtFR8Xf+zt8p1//zf/5Puc6LjC0My5M4sYYQQh/1vMnLs+b3LnjJJp558zAnDfMQwyusHf/gH/yDcv3555/LFWSs/Kf/9J/K9X/9r/81+ut//a8XP2CQaVva34y0dY2Rg8NfHy8qvCuui//23/5buf6Vv/JXytXr1sTgR6Q6PqAeyfbLL7+Mjxn7ZCeeMK4q79/+2387Tut1YIhKV5+c6qMLGa7SmTzy+7GnH4fiZOh6eeQLISwGnwfwdz3PJ4G/8Tf+Rrmim+acaScXf/tv/+1y/e///b+X67KJ4RXWDna4OM7D2GKhx/2rf/WvShwGC/DWxO4YYBT883/+z8uWNFvT5JNRoHxsVf+tv/W3ir8LbXtTdx+3bt0qkwXlc8z59//+3x/XzT3HpBxBEsZbGjoAW+naxdMOF29wTDaTZNdVOlPeX/trf21cFpOQjmH/7t/9u6P/8B/+Q0njBuu//Jf/soRRNshwVdnUR7tQJvXIwNJxqI5+tftIG0hW2pS+CSEcHuYTPc9/+S//5bKzzvPHXHKSYI5lvgTmnFmMS73kMl8NQQyvsJboOO9//s//WVzXcaOQUaCHE0ND3wwIDAsmttq40vcFMjgmoW8OcJQFqhvjCeMH2I1zMLAwdGTsOLPIjt5Mvhhtqhd++OGH1vdNPxmiPjmRB8fkBvXERX1eJmBMkY7ycOhL/Wp3jEuVR9+EEBaDjBJecHgh0jN90tBcLAOMOWXSS5yOJ5mvhiCGV1hLuo4btd2MkbOxsVH8h0VldhlF88AEUhtlq4COI5FrFv7f//t/ra8f7XjhTtrbeAhHDUYGL1LMcycR5iS96DJXalf9//7f/1uuXeiIUfP1sonhFdYS3vx4u2HnRUaWroT7m6DC9VZEHh3HTYPvDXjw2fI+iPGl7xV0hMeuUL0jN4nDyD4LvEmyYzbPkaB2BH0nkLbRN3UyhinzpH1/EsJRormD540XHO0snzR0OgB62fNvdh3ahPmZeX+oF73ffW1o/YXf/e53M7+9hnCcYVHnTcc/vOSD03/0j/7RvgeQh1NviDyg+paK9Dpe46gQowKDga18IO0/+2f/bJxX314Jzw9MhnXdyImBAxhNTJpdYeDlqa4+2fnwVN93saPG22EtO05p8Kts1an6qIsrDh0+f/48PtKk7Lp+DC6fHDXnuKx1W4VwUphnl3hR6NnWc+VG19CyLFN/5kZeOH1+0fxWz7fC59BlgL5uasXwCiGEEAbkKAyvVWLd9K8Nrxw1hhBCCCEMRAyvEEIIIYSBiOEVQgghhDAQMbxCCCGEEAYihlcIIYQQwkDE8AohhBBCGIgYXiGEEEIIAxHDK4QQQghhIGJ4hRBCCCEMRAyvEEIIIYSBiOEVQgghhDAQMbxCCCGEEAYihlcIIYQQwkDE8AohhBBCGIjffW1o/YXf/e53rS+EEEIIIRwWN7U6Da8qKIQQQggLYt3X2XXTv9Y3R40hhBBCCAMRwyuEEEIIYSBieIUQQgghDMTchtcPP/xQziv73Lz8+uuv47w//fRTGzoc33///bh+ZAkhhBBCWBYH2vHa3d0tH4ptbGx03s/DuXPnRltbW+3dsPz444+jV69ejba3t8v9ly9fyjWEEEIIYRnMbXg9fvy4GEtdvHz5svUdDz5+/FiuFy5cKIbjpUuXyn0IIYQQwjJY2jde33333fgIj+M84Ud7HFvWsAul+ProUcecfWWDwkkDv/zyyzhMfuKo58mTJyXN+fPnx8eMSqv0gBwexpV6JQdyyU+50kEyCKVRfvCyXXfH24w0wssLIYQQwuqzFMML44Djx52dndHm5mY5zsOwweFXXB86urxx40Yb8g1220DxHBFSngw0DBSOO4nDKEEOdrGQAS5fvjw+Vrx37944nPLYxSM/Rhj5iSM9IIeOUW/fvj0+Gn327Fm5YsC9fv265L1//34JIw3lynjraxMvmx04ySedyKc2I93Tp09LuIxWydplxIYQQghhtViK4YVRg0Hw/Pnz8a4S309h3GA8YKB8/vx5bEgJDBkMl2nfWpEfOCKEt2/fjo06HDtAXN+8eVPiBUYPhs6HDx/akN9Qfu1SnT17tlzrXTcMLPTzY1UMHz9+JV6gJ/S1iXP37t3W9xsytCifOiU7ZWCMoasMvxBCCCGsNksxvDBiMAiuXLky3lUSGA8YQDdv3ty3S4NxgVHFrtJB0Y4XbpW+OZvUJl18+vSp9fWj3TlclzEZQgghhNViKYbXo0ePypUdKd+JwfhgR4njP47U6l0ajBIMCHZy9A1UF8TD+/fvy5V8MtrYtaIemFRGjXbjZMDow/v6uPOg9LXJNK5du1au+rZLxiqy0g46ypxH1xBCCCEcEV8rOoI6IZ27xpBqY75+3draGodvbm6Wa2MUfW0MhfG98hCme9zOzs7YT54ahStNY4C0MV/3lQV95bmMOPKCh5EXkFNhXp/L4Xp5OA762sTLJo3nlUzUqTBvZ4V52hBCCKsNc/Y6s2761/oeux/JRr7GOMnRWgghhGPJqq+zy2bd9K/1Xdp/JxFCCCGEEPZyrAwvfd+0O+UbsBBCCCGEVeTYHTWGEEIIx5kcNeaoMYQQQgghDEAMrxBCCCGEgYjhFUIIIYQwEDG8QgghhBAGovPj+hBCCCGEsBjc1MpfNYYQQggDkr9qzF81hhBCCCGEAYjhFUIIIYQwEDG8QgghhBPAr7/+Wo61cPqlly745Relw3333XdtzKj4Ff7LL7+0oasLcv7000/tXTfSqatNpKvry1Vh3jaLYm7Dq+6wVQG5+hqfhjvqAURb8VCsAz/++OPEh37RaLJZBMju47tv3KCf0pBn1oeT9JPGwaRxPAvI0Zd/Wt3LgjZcxuQVQtgL8xLfEuGePHnSOX8xBzx48GCcjp/gu3btWokj/+vXr0v41tbW6PLlyyV8VWFOmwZz6sOHD4tO4PMjc7faAXfp0qUSrvTKs/D1rCl4Dx1B+2g65OvGxkZ7d/Rsbm4Wube3t9uQ30BO4nZ2dtqQ2aDMRXH+/PkiQzPA25CTC32Arotsvy7oT/U39eHmgb5gHHdB2ZPKo16Nf/RclK6TxvEszJPf228ZLLv/QzjOzDtfzYqvMX1rdL0OMQ/0rY/IuYx1a5H6s772zWXI7nWhJ+lBcbhJcyFxh7V3an1PxFHj48ePR03DtHd7efny5ahp6PZuNrCCF8mHDx9a38nnxo0b5U1p2fBGIpoHqPXNzqNHj1rf/Hz69Gl07ty54mfs4RbBpHE8C+Sfdax7+y2aRT8/IYTZ0LzErg67Wl0ojXj27Nl4p6eLOv1x4v3793vmxFOnTu1ZLxobqNzfvHlz4ry16DZYqOHl56JsZ3K8gJ+tPtAxJfhRjW/jkUfHJV3bpIQrX9fxheImNeKk/OS7f/9+2aYlDegoS67vuIY4l6/vmIo6lEZtA8ijYxmV1QVxLhN51J5qS5Wh8lVnXabSEe9yUb50UR6l9TAgXPVThqM+dz2F6pP8QnUoroayXr16VR4WL5e0yqc+8jAcUBf9Sz8j+zyQ18cH5dMWLofqV53SQWFCbaMyHO8X4f3j6RXe1cbC6+5rP9HVZkA4cnl8DbLUz4+3j+Tw9sGvtnB9vR4fHyGEfniWeLY5JuTZmkafUcHzN8RL9LLpm+OlN1eML+atLt6+fTu6e/due7cgvm18/UZH0D4mHTWyledx+LWNqe08bfHVfuJxCuva/mSbUOHupx6VQxh+yoG+PH3HROjn4eQRyKd6HNIQLt0pw/O5PF1+ya/7WgahenBAGvzIJb2Ftz1lqv1rKFPykMbrVR7CvCzq4V5yex6X3cNr1FZclQ6/ZJE+undIJ9kkj5enuD4/9SFnF3U71rh+Squ6u/pH9Xg4+TzcZeQenQhTue6XvnUayaKyHMJxgjxd6cDHLTJqfKgM1Uc4ddZMah+VoToI5550yKNw1ScI75M3hOOGj+1lQR16Dvvgmep7rqblPQyL1H/S3EC45h6o5xUHfYl3NC8dlrrOhR81smXJ27QsbY7Znj9/Xvwc0QAWZlN3eRNuGq2EwYULF4qlzjEQ8V3bn5THdiFvwU0jtaHfaBqoXMnXNHbZZnSQiTy8CZCft/JpbwR6yxccpVE24Y6OE3XsdO/evVJXV/no9uXLlyKD0JFo09FjS7wrr+px3ZsBU+SiXRy2mnWk9OLFi5KmCz6sJB7oI9oF6B/6BAhTfyBfs7iO3r17N5b71q1bJc5Bv2nHcKShDNKhL/pJf+pDt7of+6AcobFGGHpTD+Ny0SCjxh109c/Hjx/L1cPIxxsWb6eMB+8bylMbqDzeupAfPfTMMIY4Jrh9+3a5p0x/nhyvexL0uT60BcaxYGxSvrfz58+fW183dftIDukFjCXSnT59ug35dkQA6Isjn/o0hDAdf+76YF7pWheYlxa+y3MEsH75vM+cyfrdh+ZdYD1i/exbNw/DUr7x0mLJJM7CoIXcYTKl031BQGkWoStXrpR48tcwIFgYSNe3yAifyB3yyvki0ocvEosAQ05/NbFMWMyQfZpxidH05s2b9m5UBiZt798ywbRyHPqc/qkNVIe2pw3oa/oVZjUQZkVHoNQz6YE7CpAJ4xj5/IitDwwU8sjRv8tgnn5eJvSX6+tGYAhhMqy9XS/ETtezzpzJvKS5/zgf86OD1jNg3dWLqtO1TmFnyD4g/6S1bF6WYnhhONHpLNwsDijOwnLx4sUSjxKE1bshhJMOCxNrHcPMYZBgvU4zhGgg0tQLE52AMeADadqgogyMAXUcdJUt9KYuHd1wAXXeLAbfImCgMYDOnDnThuwHXWhX2v769etlYNZtjy7+QTqGGmn7wPimnSb9ObJ2EllUqV/940YIf9p8mDcODMBFG3OLgHHA2KPt2Ulyw7cLnil2yDRRaiKgzfQRLXHoyq7xQY0nvSFqnFIO/VSP42UjOfTcIccsxmkI6wrPLC9xcswLWqcUp+caeLZqI4RnnTlTp0K4acbbUYK8mvM0V9S6stYSTxjzmNYT5hPpyDope0T5KVfx3pYLoVn09tARtIdmAS5p5PogrjGeip9rs6AWv1B+wnUlnZffKN6m/g2PV16lUzhOdJXn6TjDrUEOz4PzPH0Q5/UJyakwv5ff8/GNjPycOzuet04nP2kcwqZBfmQQ5KnbxutWXN2+3nbI19UeotZZKAynPquRvnVbejsgS90uXJHd287xcKV1vDzFy0+cy+NluZ805HP9ufeyPb36pSsMVCdXHHrXKB/xoLp077hOivdngLpr2R0fAz///PPYX/eF37tfurkcOA+r6wzhOKHxvK6sm/61vvmR7AUiK3no3YFpsLMy7VurEEIIw7Du6+y66V/reyL+H6/QD9uvq7xVHEIIIawTMbwWhL5XOj/lg/+hYJcLK5tvtRZ6Nh1CCCGEA5OjxhBCCGFActSYo8YQQgghhDAAMbxCCCGEEAYihlcIIYQQwkB0fuMVQgghhBAWg5ta+bg+hBBCGJB8XJ+P60MIIYQQwgDE8AohhBBCGIgYXiGEEMIJgB+T51gLx3+i3Yenwzn6z7dxB/2x/SFBTv1Adh/8B+ekq9vEfyjb4/RD2Tj95+iLZG7D6/vvvx8LhKuhAUgzBDSI/9r6IqHceRtcg3mWwTpruqFYZlvOAmNm2sPjDwOOh2YIVO880J7T9FkWk8auxmgI4eSB8cC3RLgnT570zunXrl0bbW9vl3Sbm5tjo4M568qVKyV8a2trn6Gyaswyl7G2PHz4cPyNleZl2ub+/fud7aX0yrPodpjb8Hr58mXpkI2NjbFQAqFv3rzZ3i2GSQp/+PBhaT+HQ7mUPw8M5j5Y8GQozGvQLRsGJj/ufVTQx69evWrvvtHV7/SJxtzOzs7o3r17xb9sLl++3PpmA9knteeiH2KHcTZJXn7AvX5uQwgng8ePH7e+UVmjJ62Pp0+fLtezZ8+WK1y4cGF048aN4r948eLo6tWrxb+qMJdN+pk+5kPWFunE7xY/ePCg+MHzuh87R2CELXqTZKFHjXQyVvSiwDJdtMLL5PXr161vP48ePWp93wzGVYJBdpS/MclkwSQhhtrJmhWMvHlAn772XPaYxrCaV94QwsmA5x+YZ9zAqLlz587o9u3bxf/06dOxwab88Pz588FebpfF+/fv98zFp06dGr8UyyhlvWHTiI2TPkPV22URLPUbL97s2QrUDg8LDvc4P47knoGiOBqBe3bPsFYJ61qsPFxHVVwJ71u8daTGlXTkIS1+l0llCdXFFactSVCdblw5tAPbmGxr+m7Xly9fJpaHq/WuZVBalas2J52nVRhIf+X1OjwPbVBDuOpQvNK7bkpDXbSv5yGsTi9ISzvRXqSZF8qUfqpPSAYcaYTS1vUpnAnI0XjBefld48ghbT2m+/qoxmXHLyizKxw8TqhthKdBfpDe0kN1q82U3ssJIawGPLfMM+x8980nGFQYGjzH9YaB5mfm4HpOOY5MmqfYBGG9YVfLdwudt2/fju7evdveLYivFR1B+9ja2vq6sbHR3u1le3t7HLe5uVmugrIba7P4SUM5jTVawpVHYeBl1Sgf5VEPflzztt+bjzClIx914efKvcIpA7/KUF04oD7y1H6VJx0dTwekk5595aGH0jiU7+HkIS0gu+r3NMRLNulCWkFatZ2HO6Qhn+uhfIAcOOrgCsQrPe0pOWvdPI70yt9FLbvo6l+V437ykgbqetXnHk4+pSdMadSWXD1c5Su/4+nA24A41eOontpPPcrvsqh+7wPClEZ5PD8QRxggI+mFwlUWUL7qCCHMjp7hZeJzQA3PMPON5hw90w7zIXE+DyyKRerPnNQ110I932oOFGof0vhcKNROh6XWd2k7XliZbiXqbblRrljTvPV//PhxfOwmaxNLvGmcXktd+HGdjqqaBhpvFXYd5+lIrWnM8dYh+ajTtxLrI1OVhVwC2YG3guvXrxe/rrPibxoqjzAscNqItxavU0hWtSlt9ezZs+J/9+5dia93NjjjRld22dCfdqi3VdnZwbrv225VO0hP6kU+3qz0hkQY9aMH9ZNnqO3qrv7VOHrz5s14F03fQBHHOKSdCafdkVfh/l2AoH20Y0VdQJvS/tq6p/0UN4m+PlK/CnRpntWyO+Xl0tccGYDSSG/S+Rvc58+fSxxtI8hP/6ELDkgHfNvBNj1QLzr19XcIYfXwNayGZ1jfc5FOc5fDvD3LPLbKoCPztWCuZo4F5jXNX6wdzMXa9Qfi2AnTOrBIlmJ4SVFN3IJOZHGQ69vaW3dYHL2dumDBZeFkkeYsnzavF8Eu43MSGEv1tvMsuKz6KJG6KYsFelW2q5lgXFYZKRgTCpulzZo3wT3l9BmqszBrH9GOGH21IS6D/aA0b3x7dNEkg3EtY77G06u/QwirBc+vvzTW+PrsL4BCL4aaJ48jyI6hhZEFGFJuZLpRBmfOnGl93/5YTvMb+esX4sOwFMMLRVlQMAhkDLA4sWi4RekLsr9dk/+4dDbGpL7tkg6T/rpxGuT1gdFntPAXJ3wUifFF27KAvnjxYjxw1N4acECfTDISeFAZmLMaSvQR+nt6/PQ53xkQjwxuzH369KlcZUTMWtdhYQeHnS3BOEROxlotv8aexiptDOjEn1qzM6ZxrQeSPPqYlTh0oz6l62LWPtIzUb+oMAZ81wk55pkclF95uEoWtQH3+uunvv4OIRw9PL/avcYxH2kuUZyedV7utdPPVQYGc5zysxas+osVhqHmWs1dta7oIF2Zw/RyyZV1U/oqTvkpV3HelguheWvdQ0fQHpoFoKSRc5rOHIdzdorTfaPE+HzVwwC/lyu8PPxOswCM47wenUvjKNPxOjyd58fVenhdng+/66TypZfj+SaVB7WcfVCO4iWzU7d3Hab28fpq3R2X21EYjvzU4WVut2fkXBVGPOWBtz/5PZ23pcuGq9umbjf5pYfX47q5XpLV61K5wssmTqgcrjiV5Xi5aivd4/pQvNcBLgthdf96m/z888974sDbWmUKxdUoPQ4dgLz1eAkhdNP1XK0T66Z/re9K/Ei2rEu9ZYcQQggnlaNYZ1eJddO/1nep/51ECCGEEEL4jSM3vPRR3/lj/tcTIYQQQgjTWImjxhBCCGFdyFFjjhpDCCGEEMIAxPAKIYQQQhiIGF4hhBBCCAMRwyuEEEIIYSA6P64PIYQQQgiLwU2t/FVjCCGEMCD5q8b8VWMIIYQQQhiAGF4hhBBCCAMRwyuEEEI4YUz7XptfjSHNDz/80Ib8BmHff/99e7faoMNPP/3U3nUzSVfC5X755Zc2dFTKVLh+YWdRzG140Rku6KqAXH2NT6N5g54kGEg//vhjezcf9N+vv/7a3u1lUnuGEEJYXboMDIf5/eHDh+PvjnyuJ++TJ0/au9VmFhtkkq6snYTLXbp0qY0ZjT59+jQO//DhQxu6IJpC99ARtI+tra2vGxsb7d3Rs7m5WeTe3t5uQ34DOYnb2dlpQ2aDMpcJbbi7u9veHT203bxtFEIIYX5mWWcPCvM460tfHaw7Hkf68+fPt3ffWPYav0j9kb1r7YdJuioOV+dX+y1KzrqcE3HU+Pjx41EzSNq7vbx8+XLuH+A+6A7SPNy/f7/1rQYPHjxofSGEEI4rz58/H12/fr2928/79+/3rImnTp0aNUZIe3eymKZrYwOV+5s3b+5Z92k/4hoDbOLJ0EFZqOHFcZ6OIBFU56ps9QFXbQ2ynam0vi1KHhzbgV3Hg4QrX9e5q+ImGU+T8pMPo4itVtIAuigPrq8TumT3NpGe+IEBUcupulxP1UdatZtkr48ECVe+Olx5KUf1KI7Bd/ny5bGMhHn7q0zVCyqPdJPaO4QQwvJhHr57925714/P4yedPl3PnTs3vrL++WaI4u7du1eMr0ePHpX7RbFQw4vz0Z2dnbL7hOCci+LXbsrt27eLFcmij2Eja1PnyRgKnMW+fv26WKBdUBZ1kBfcOCAP4cTTiF0GEp2g/NeuXRsbGkINvbm5Oa6DdPhx29vbnTtoXbJTP8aM8hJHOpWL7tTnqOy3b9+WdMiBjOSTQUg4bUv4q1evSnrg/tmzZyVeVjztg3HGPRBHna4DZXFPu7B76Omhq83UtoTRryGEEI4OzckyGsLs0GastV02A+tlV/hhWPhRI8YXxoAEZVFn6xP4WA1QkgUbY8INgAsXLhRjAeuSeP/QTVAe24XstLhxABhFQD4MPrYZHWQiD8YQ+TFipjUohotbzDdu3Chlu8EHXbKrfu0WUbfaoA/phAEEvL3QntQrg1DUR6zoozajjUn/7t278XHrrVu3ShzUbef48Wxfm1E+hiRtQ5/UBmQIIYThePHiRXk5Z57W/I2/a63yF/YvX77sWUdOEvPq2me09oUflKV844VxgNGBYcUOjHa0HAYEuzpuAMggu3LlSoknfw27MdqBcqOti9OnT7e+vZBXDiNjGhgW0+iTnU72+pZtoEwzJA+K66A2o10wvtC33jkMIYQwHKwtmqO1ruKvNzBYq1iXtEZxUnNSTy1m1bU2Th3WNt+0WARLMbwwPjC42N3R7hNnzxcvXizxNAJh2tURhJOO3R12rzDMHIwKrNdphhCNSJquAYex5kbCNIOBMhjE6jjoKrtLdlnbyov8s34LpYHAW4zvck2CNvWz6Ddv3kz8yHIW+toMXTCCiad9MMBCCCGsHqwnvvvFyzMnNIQxh7NuCdYods5Yu5jjVxlOXFh/0EXr7Ky6oidhOAwyt0coV3HYM/V6f2gai3gPHUF7aBb3kkauD+L0J5pcm8W7+IXyE64r6bz8pkHb1L/h8cqrdArHia7yPN1Ox3+hgByeB+d5uuiTnfK78kr2xqhqQ76hulSW2s1l2traKmHkVZj0ULkeNqkNVL7ScJ2UHke5hHs65APlDyGE0A1z5jqzbvrX+uZHslcMdpIaYyh9EEIIJ5R1X2fXTf9a36UcNYYQQgghhP3E8Fox2O0C/0vKEEIIIZwMctQYQgghDEiOGnPUGEIIIYQQBiCGVwghhBDCQMTwCiGEEEIYiM5vvEIIIYQQwmJwUysf14cQQggDko/r83F9CCGEEEIYgBheIYQQQggDEcMrhBBCOGFM+l7bfyD6hx9+aEO//WSdwuVIexxAjy6Za51qyDf0j4HPbXgh4CQljgrk0q+T1/C/wOuXysPxRw/SYfFfoAefjOSfBg/toiamRekVQlhv3Jiq0VrIN0e7u7ujJ0+ejNfO9+/fl3C5ra2t0cWLF0vcKoP8r1+/Hut0//79Mp/CtWvXRtvb2yVuc3NzT9vgR/+hmdvwevnyZemMjY2NosgqQOO9evWqvdsLBhkdMS+TBu5J4bjqqJ9VOgzofufOnTKGKY/7p0+fjiebN2/ezDS+Hz9+PLp37157Nz/eB4vQK4Sw3mBYnT17tr3bz6VLl8Zz1rlz54ox8unTp3J/4cKFchUfP34s6Y8D+pk9dKo5ffp0udbtwvzNfD80J+KokcbDEOwCQ3HeBe24bK0ehuOs40EM6Rrehs6cOVP8Hz58GF25cmX84DIpMW6WDTL429Yi9AohrDfPnz8fXb9+vb2bDc2FbrRox+g4cOPGjbL5ws4XDmNKuvCCffv27eLn5Rp74ahZqOGFpa2jGjpNRzk6P9UxJfCmr7T+1k8eHI3XdTxIuPJpoXQUN8mwmJSffGxTsiCSBtBFeXB9A9Jlq3VSOGkA3QhXm6i9vF3kiJMMassaT6920z2oXPTr0tEhLWmUh6v3LbiuXl/dpp4O5KdMIL3y9PWtyzKp/K5+UT6c5KQMHtKbN2+WcMrHTxj3lKn+EV6PZAf6gzhBHqUTSqO+pj5k1QsBYV4GqBz1N3m60oUQgmCeuHv3bns3G8xFGC41HDveunWrvVt9eHFlHn/79u2eUwj8HDcyf3IcuRJ8regI2kdjTX7d2Nho7/ays7OzJw4/YbC9vV2uTQON63E/8TiFKZ/TLFbjcPdTj8ohDD/lQF+ezc3N4mrQz8PJI5BP9TiUqXSSnyvp1R7yKx5Xy+/1kt7vkasLlQmuu+oRpFMZtY6CcMlG/SpPOqAj4UCcwqFPvloPypC8Kot7L4v03BMveYTr1aWDcB2lh1DZAr/qV/nqT/J29S1l41c5XiZ1c680OMrpqkfU96QjDxDn8oYQji/+nC8K5gjNwfVc0gfpSduF5qllsAz9NdczV7vsmndx1Ks5VWiuXia1vgs/auQ8mJ0D7T5wjMPWJ+gcmS3Apu7y9t40UgkDzpexWB89elTiu86WKe/UqVPFem0GTBv6jaZhy5V8TUMWi91BJvJcvny55GfHp2uXxKl3PngzoGztnoh3796VLU2QflxJz7EVuyTopvim84vu0lFHov6GQV4sdGTEaTvYIZw20bYq5TUDcJ/us8LbAfmbwbjnLUhHb94Wkr1uixr6VUdqau8XL16Uq8YE91evXi1+YDuYcYQMyIJMNbQpR4R98J2WdvXoc5jW36D+EX19Wx9x+y4aO4r0i9IwNtVehM/CgwcPRg8fPix+2sf7I4QQHOYI5h3mH62r+PvmZ+2ea+2o6QtfRdjpE8yvOOnH3M8axPzJPKxjx6NkKd94aeFH8WfPno0XXYcBwZagG09a1FhMie86VuHohW1D0rnR1oU+qKshr9ws3/LMulDyIWINgx5d0EmG4Tyw4PNATVp4awN0SBjEGNb0Vd93BfSrjFX0YExw1l4bk13tNwmMUsbQJGhz7++DTiazykZfqK5Zx00fGGqUMYuxGEJYb3hp1tyjNQG/Xvgc5mI/kvPPJ4D5fNJL7SpSHyP6+u8bEb55cFQsxfCiw1hc2c2g01l0sUj1Z6l0KmH1R26Ek06Wab2osgCxqzBtQWNQkaYecCy6GGs+yOoBV0MZDGI3ArvKRjffQUMGHEZJvXs0DzwYvMX0LfzSyS1+BqDXJ5loO8pCrkVBPdRHf08yatjN0s6n2g4jTH9F09V+XbtcjsaB941DndplBNroIEZMX9/WMKbnGVuzwI4XLxpdu50hhDALzFfa/WIeZBdIpwG4Gubzg65ZRwHrpH9fy4aF1hlOL3QSwdU3W2gL1kTWRn1PC/j9fuE0FvEeOoL20CwuJY1cH8Q1xlPxc22Mg+IXyk+4rqTz8huDp039Gx6vvEqncJzoKs/T1ee9gByeB+d5+uCsWGmkL+V7GNc//uM/HochH0zSuzFAxm3Zh/LW+b1c/MgItY7Cdaj18bK83UincvtQGyof6aW7cJkU52GqQ/caM7rvaiPaTvH4Qf2AU5voHud9Jjk8jfrWy5ZeXjbyeBovQ+V2xal8QZgjnetxEkI4HtTP9LqxbvrX+uZHso8B7Jyswp/A9sFbA8eMBz3GC5NZ9f4PIczHuq+z66Z/re+J+H+8wtHCMWiMruXAMepx+pPuEEIIk4nhtaLoTB63irsdfO8k+eb9f2PCdNjlom35zrH+njCEEMLxJUeNIYQQwoCs+zq7bvrX+mbHK4QQQghhIGJ4hRBCCCEMRAyvEEIIIYSBiOEVQgghhDAQnR/XhxBCCCGExeCmVv6qMYQQQhiQ/FVj/qoxhBBCCCEMQAyvEEIIIYSBiOEVQgghnAD8F0X49Ytp8JNkpP3uu+/KveeX47d4VxlkRI9J6Jdg0K+Gdvr+++/bu9+gTdQG5F8kcxteCChhcMcdDbSuDlk2DJauDj9qGGR6EIGBueoPXwghrDvM1XxLhHvy5MlEg4G15+bNmyXthw8fStj79+/H+XFbW1ujixcvlrhVZBYbhDa4fPlye7cX2ot2qiH82rVrpQ22t7dHt2/fbmMWw9yG18uXL0tnbGxsFKGOOzTukNCh4saNG6U9VwkMUB+kGFxdAzOEEMJq4b/ryxrd9zuvepGu1/ALFy60vm98/PhxpX8rFvnPnz/f3nWD/Lu7u+3dXmgv7Jkuzp49W66nT58u10Wy9keNr1+/bn3L5zjsGp07d260s7PT3o1G9+7dG21ubrZ3IYQQVhXmb+A05cGDB8Xfxf3790tanVzpxEf54ShOgVaFW7dulTaiDZ4/fz569uxZG7MYFm546fisPjtWB/vWp5+h6oxWx1xcFScwXBTmR3Sqy53q8aNRH0gKf/ToURuyHx2xcSUt11our1t14vejOqAcOpLdI+VV2dCnt9pI7QNeZ5cxJ90ou5ZP5StfX/uEEEI4fjCnc4TIyUXXnM4awC7RlStXyo4RL9ZdJz8cO2KArCPsknHEqHZa+K5f0/B76Ajax9bW1teNjY327jd2dnZKflzTmSVM90CYwhulxmXIv7u7O06vuEbxUi4QTppJfvJRHlAXsgJhlAUeztXzC4XjyCvdXC7VQ5zCQWXXEO76Ux73fXpzTxz5FE5d0gNIo/ZxSK/wWr5p7VPX4elCCCEcDubtZUMdWm8c5npfD7T+1HiaRbNI/X0t7kM61us8+Prq+Np82PWv1nehO16yEhsl9pw1Nwt5uerMFPR9E7syWOegY66mIfd8+/T58+dybeQdffnypezOOKQnXHz69KlcOUZkl4n01NE0Ygln1+n69evFr2uNjtiaBi+yCsnlO1qyhnmTmBXKpGzo05u2JO7MmTNtyGj07t27ood2qUDt41y9erVskQJ5+HiStx9k1Jl1X/uEEEI43rB+dMH8r4/pJ+HHjusGu4bsdNEGrIs6dlwUR/aNl468UK5vgNRg7Dx8+LAYYA7nr2yryhDBaBIYNKSXWxb81QOGDkd7fcbcosAgdJ3cMBTI4N+vsZX84sWLYoT5tulQ7RNCCGE4WBe7jgo1/+vzFdYF1hSHONbmdebt27et7xuLNESPzPDCSKl3kyah3aSuvwKUMYbzeIwN/zNQfU/FzpK+7eIcGw77143ogaHDYF/mmwJ/2ut/JsxVD5AjGfiWizw8gG/evClhoq99QgghHC+0mSHHx/V+GkOY1g3WKk45CGNd8BMqYB2bdW0+StiMYUcKXbQO1rqyU8WaD1x950rfXr969arscgnsCH2PTR6d2i2MxljZQ0fQHjgLJY2c0wg3Dud8tBF4z738nJd6WqX74z/+43EY9XhdpPfy5AcPlxNehs5pm44ahymeMIe0nk9+6qrlEqRTHV1wDq18f/Znfzb2Nx0/9td6e7vhr8tBnj6QxePxk9ep26duG9e9zhtCCGF+mE/XmXXTv9b3RPxINlarHy/K0vUjtSFADo741vlsPIQQwmSO4zq7SNZN/1rfY///eGHs8J+8ORxjDm10AXLE6AohhBBCHydix0vnvIKP9Yc6n/bzY2SI4RVCCGES2fFa7x2vE2F4hRBCCMeFGF7rbXgd+6PGEEIIIYTjQgyvEEIIIYSBiOEVQgghhDAQnd94hRBCCCGExeCmVj6uDyGEEAYkH9fn4/oQQgghhDAAMbxCCCGEEAYihlcIIYRwAuA/9OZYC/fDDz+0ofvxdDiHX4NRuH54+jiDDq6rflIQ6jj/oexlciDDq+40d/wv8odFvy6O818SF3VjuZs02LogPfkWIfc80MHUO1RHhxBCONmwnvEtEe7Jkyd7jAzn2rVr5RdeSLe5uTleN1lb37x5U8L5JZabN2/2lnEcwH5AB3SUrrdv325jR6O3b9+2vm943DI5kOH1/v370mk42NraKkrt7OyM7ty5U8ImgUU9CX5nkQbip3i6foKHxmJQkAaol/qR49atWyVsVh4/flzqYSAOBcbWhw8fitxcQwghhMPCeiY2NjYm/mbx6dOny/Xs2bPlCqytMj5Ye1lj+e3j48qXL1/KVRs4rqvAdpAb6qcGqWwPHUH7aIyecm06paRvDIhyPwvkwU2jMYZ606l+0swi7yQoizIao60NWS6NsTp3m4UQQjg5HHbdmgbrzKQ1hvWO9RN0hcZY27MW4p9lvZ6XZevvyE7QVe2itVhumdTlH2jHS7tQsiK7rGo/J9Y2Jn62P3E6YtNRH/d+7MaO1pUrV9q7vah+0mDV1+golPK4aqvUjzCRj21VWcQXL17cc4Tp26se7n4/BvVw7ehRP+FqC65se8LDhw/LFVwuP/Ks8ysd4dJRbStdHYVxVTqXReUpTiiN0okuHUMIIawOzPesM5cvX96zRjn37t0rpzzM5a9fv25DR6OrV6+Onj592t6dDDhVaoyuYi849TEjbcEaNwitATamI6gX0rq1LLQTxm4SFrP8sjDxg8dRjixrLFLCJ1nsStNljSu8rg9rXvLir+WjzC59gPIkI2nJozcDwigPFK5yJYPK5aq0oHiu0gl/X378xHGPX3XrXroSzj0oD3h95FMZwvMRrzjVA15eCCGE+dAcu0zqud1hrWEN0Hrga63WEjnSLBrKHQr0kb7SCbRWgtZbX5sXSa3vPu1nbRAZH3XHynjQwuwLufvB8+NXB6sRJqFGrAeF8iIffhks0NW4+ElDOZ62hjjJKt3JIzkIoyz8QDt4HkG8h7k8Kou8XfnVtoTXfukBkg995ScNeBu4HqAylVb06RhCCGF+hphDmbfr9UdoPgfSae1wWAe6whfBUGuI1i6h9Uu6O5Pa67DU+h74v5PgA3uojwP1IR5bmcCxYqNs8bOlKb+29PgYXsd6+tjv48ePnUeIjrYJL1y4UK6CbVLychzJX2f40R0yNY07evXq1fiojG3IphPK1ixXqI/e2K4lTrq+ePGiXKn72bNnxd8M0HJt2rRc3717V/L4x/7SU+WoDR48eFCuKouj2678hAHHlJ8/fx77SY9O+gMByXf9+vVxP506dapcaRPgI0zFqd3pO/Soj477dAwhhLCaMG9P+mMzzf9Q/1U/axNHlcfxj7/0qYwfG2rt1bEj9oE+xdFxLDaF/3HCMjmw4dVn+IAWaBkwKCPjhTNkB4NAf0WhBR8DrU5XQxo1YA1hNDrGiMrhng7hrxZk1Emmra2t8V9o0hG1ManvwDBQyHP//v2SR3VTHsbIy5cvxwMYA4dwN2JkONVtRhtIXsnRlR+DFJ3RQe2PvzboBPJhUHo7UUdj1Zd7leF10B7CH8YuHUMIIawG+mZXjhd6ze2K01qxs7NT1gbCuDKvA2s2YRhtJ+EFm/WR9Q4jEr1Y32RMyu5gfSSuXj+XStO4e+gI2kMjeEnjjjCh4ypco1Abuj/c79nek19bg3IcjTl1vNcBjYFQwr1M0nCvYzYcSAaugJ90NX48x9VlquURdTqg7Fpez+vt2JWfvMgAXOX3tiTMZfJ2+Pnnn8tV5XocZdR9K3n6dAwhhDA/6z6Prpv+tb75kewZYEuSHaLDbkOyU8Rx4FDbmSGEEFaPdV9n103/Wt+5DS/iQ4B1enBCCGFRxPBab8Nr7m+8yLwubte+d4Kd9n/In8eRRxwk/yq7EEIIIcxHjhpDCCGEAcmOV3a8QgghhBDCAMTwCiGEEEIYiBheIYQQQggDEcMrhBBCCGEgOj+uDyGEEEIIi8FNrfxVYwghhDAg+avG/FVjCCGEEEIYgBheIYQQQggDEcMrhBBCCGEgDmV4cW4pxw9Jww8//FCux51ffvllrBv+ZfPjjz+O6/vpp5/a0BBCCCGcJA5keMkoAT4Ywz148KCE/frrryX8MFD+YY0dDJnDcOnSpdHm5mZ7t1zQ9f79+6Pt7e1y/+nTp3INIYQQwsniQIbX5cuXy9V/RBpDZVF/paDyD4oMmePC58+fW983Q/bevXvtXQghhBBOEnMbXjoGO3/+/OjcuXPF77x8+bL1jUbffffdvuMzjiK5J05+HVOyW8Y9YHxp10rpcBhVSocjTvVQDvXIcCPsP/7H/zhOSxxplFYoDEdZ00AGTy+/7/Z5mQpXWmSWTo8fPx7dvHmzxHNVO3l+tYPrTTqV58eU8hPn6X0H0dsTJ3Tv5Uke8HBvv7p/QgghhNDD14qOoD1sbW2VNBsbG21IN5ubm+OylGd3d7fc48dtb2+P0xG3s7MzvscPpFE8/sbgK+GAnzjKV9nkU7hQ/eQnHX7Jr/K5Kp3qrmVxFEc+8DJVjvwKpxzClY8rYS4DKL90VjqPU3qu4G2hNIRJX+QFyUCaul7d44BrLTtpuuST3/snhBDCfjTHrivrpn+t79w7XmfOnCnXDx8+lGsfT548KbtioDwvXrwoVyDuxo0b7d03OK6sefbsWbmSnh2hZoEf7yAp7uPHj+Pdt64yJoEMTTuU76p0POlHf9M4ffp0uTYGyujVq1fF//Tp03JlB4gyFS4aI2hcb5e8yo9OKv/58+flKhojZ1yG48eUDx8+bH3fdstAR8L0iXba6m/KGsOq9f2G6kce6qAM5JvUPyGEEELYy9yG14ULF8q1b4E96FHTly9fWl831MdirwUfMCIweDDyDrPYYyBBl8FxGCQvbpXgGPLt27fjj/knMc3AFl39E0IIIYS9zG14sajKQLl27Vq5Cv/uh10dFmPQjsr169fLdR6uXr1artot43siGVn4FV/L0sf79+9b3zf0/dTFixdHb968Kf550O4Yu1oYgSBZ/Hutebhz5065oqfKv3XrVrkeFmSiX65cuVKMr1khPWgXjXIwsif1TwghhBAqvlZ0BHXSLN4lrTt9hyT43kdxfP8D+jZKYfKTFvQtkeKB74w8zOvW90Y4leFhpAXd12W5DC6by6FyHaV1HVUXeLjapSvM68F16Yw+ULe5cH1dB5zXSdlen6f1ssnjZar+uh5Rt2kIIYR+mCvXmXXTv9Y3P5J9QNjZ4YizMWTm/q4shBDC+rLu6+y66V/re6D/xyuEEEIIIcxPdrwOAN82+X/yuru7mw/KQwghzER2vNZ7xyuGVwghhDAgMbzW2/DKUWMIIYQQwkDE8AohhBBCGIgYXiGEEEIIA9H5jVcIIYQQQlgMbmrl4/oQQghhQPJxfT6uDyGEEEIIAxDDK4QQQghhIGJ4hRBCCCeAX3/9tRxr4fhZu0kQ35X2u+++G4fzn4WvOsj5008/tXfdoAfpaB/H26AvjvZYNAc2vKSI3Pfff9/GrAca4LNAurpTBZ37448/tnezQXp/UBaBP7C4RZe/CHgApj1gq8hB+ngeeBaXMTkMDTp0TfSaaxbJSWmzZdPXJ4fluD7Lqw5zDd8S4fgt4b6+Y76/cuXKOO3jx49LOPlfv35dwra2tvb8QssqMsu8QBt06cH4Uxugq695mq+Je/jw4eLXw6bgPXQE7aMRcl+6jY2Nr+fPn2/vQh87Oztft7e327vZ2N3dLW0+BPThPPIdVLZ522Fzc7OMuXnbbpEgw7zoWVlW/9H+lH/cnz3mD/RgXNQQjuvioM/TSWizZTOpT5zj+CwfNX3j+bAwtgX914f6dlIaII2XuSgWqf8sa5aeedfF/Yxhn6Mp08f9Yduh1nfuHS8s5fv3748aodqQb7x8+bJcl/lmfxLAep6XR48etb7V46CyzdsOvJE1D0N7Nzy8NfEmOC/37t0bNQtNe7d4+I3Q+lk8jjB/9PXvJP0O8jydlDZbNpP6xDluz/JJRr8ZzG7OgwcPir8L+qBZ/4t/2s7vSf0dYtfr+fPnZa4GbJzGyBqdOnWq3APj9cuXL+3d4Znb8Hr//n25Xrp0qVyda9eujd68eVP8Og7jynZg31Gk0nDVEReLHH5tI6oMHI2iY7G+MpUWp61WBhf3lFWXxyDFz1WyKE1fHZTrA1ZlqVzVqzCgrFevXo1u3rw5Lperttxdb+VBFraMMXZVn+Tz+rgH6Qke36fHPKiPkFPtVMtGPapTMkkO5cfV7aD2xqk9QOGT5Ce92pG03i/eptQPaiPK9nqRU2W4DJTBVjUPI3HcKx3O66vbqEZ1Sxb8yE5a6ndUFvGuB3V7mwqXyVGYy6n24kpcl6xAnI8j5JBcqltlqHzJ0VWm64GbhNqKSbEL6q3HkfLgkANUJ/J4ewnClcfvpatTt0Wtu9qGdJ5W8gFplU5t5HL3obpw1C0U1hU+re+Acn3sdPWbUBrpS95FPsvh8NC+9AdzFv3fhYwOjGv60vtJMF62trbau5MJOjImWcfqZ32pBue3ja/f6Ajag45OumALubEMy7YfaXCkZ4uuK4/KwpGHrT382v5UWcDVj3p8W9Ahr7YEVZ7uKU/+ujzucZJnktwKpzzgqnxAuZLPwwH5pBPpiNO9x9XpVJ7azPWXHIDOgnTSl/I8Tx/e5g7lqL1Io7JcNuqWLO5XG3i5rh9X7kFty9XD1Ze1bArHST7lV1nCdcNPPBCmvFDXAa4P4CfM/ZQ3qY2I9z7ATz7pIHkc8koe4l0GhSu/113LBsTLkR5HHOWonR3yKh0oH+lVpyDO2831dFx+0vi9y+rjQ/V20TeO3K+r10c98lO+4/nqOKj7wXWnXPUjMstPWdTPlXAvlzDlV5oa0qtNPQ1X1aE+kXz41W7kx0893ncK172XAd4n7iefdHD5vd0pQ2V5uOpQnnVE7b1MqEN9NAn6Q2PLmSXvQVmk/ozLaWPJx2IX6K94pXV87B+Eurx92k9rEBTsS0NH6eFCEXXcJKVJo07XAyn8gQaUV8N0NTThpHEoX2m9LjU0EC/ZZpUbWb0uT0f+ugxR61TfA+lxXXJDfU8Zqlt51JbuJNMkJg1i4rra12UBta3S1m0ArjdluJw45O/q/y7ZCCOtUH7C63Ilq8steYE8s/Q3SC8c8dDXRrg6XH1Ut5/juuGnDPJRN/dQy4Zfekg+OZXlbatyuyCP2kN6gMoW3KsM1V1DuPIL8igtfunmZauduqjHCFCH66r+pRyhNiONhwNhffUJyQqk97pAMruT7p4X6nTE1xBWt6m3uaAOtQdlKQ/hqr9uX2T3NvR7yao87qSzp1fbuyN/XQfl+v26QbssG9pXfT4J+qfuC/qrHm+LZJH6zzKWNH4n6eTPmMa9OKy8df65jxovXLhQrl1bk3wDc/Xq1fZu8dy5c2f04sWL4m7cuNGG7qVpuNa3n1u3bo2PQqEZXEWPT58+rcQ5to4Bmn4pss3K7du3S5vUNIOnlCWnv1w5KB8+fCh9jIz1tizoWAOaQVuus9IsWHtk7TrKPgi0o5erc/zDjgW28zlap0zaWfS1EWGMTd/6R0fyA+n9mEgwzjnKAWTjGeDojSN/PYvTcP31Leaioc04skCHSW3Yd/SxCHRswF8qNRNxG/rtOzt051skjU+gP+ivd+/etSHfoI1IT9q+YzH6gXzUybc09FGt2zzPH7IoHTJ10fWNyaT5bhm4Pn1jaVnPcpiPZ8+elXluGqwfvp4ybzGm9Qx3zfUnDZ5j5i/prHkWOCJvDNjiXxRzG14IxoPFGbJPNJqgtLAtA8rme6KPHz+2IXtBNiY7/0aBBU+DigmACZL469evlwH39u3bErcKcM58kIkU/WgXDAfXlbK8LQ7zANHX9DFtTLm0aw3GHwN03jHAQon8Gk/owYNAXfpAlDjqrcfdJDBM6G+9JJBP7XGYsUA55K0XSML72ogHmTCMNUHdyEZ78UzVBoDAKCTtmTNniqyUO4uBqOfB+32Zkyh9RTv2of6gb4H28slO6F59pXbpM4IEEyXt6IsIkI+6MBRoS9VP29CHGOAKA32/hNGAvF1cvHhx9PTp0yIbY4lxz/inj2Ce5w+ZpvUR48Y/Yqdc9a/X4fPdPGjsa2zXZcw6lpb1LIfp0M68LMjR3oxDj+NKm3s6n6cY+6xDfB+m+FmMt6MCeTWWNM+7roC+jF3gqjHHvCAdebb8RYI5We3Ec37YTYt9NJPLHjqCOmFrj7RyvqXpcc1EuCed43HubxqnbHXq3rf8qGfatqLy4ZpOaUO/QX7KFl7+rHJTpsIpC3l1P0knoH7dy49DBr9XmYT3lentUOsFLieOe+lYt2GdlvIc4r1PlN/l8TIkv+dRG4C3A3g5rofK4Yqr5aZ9lI8ylR6HPB6Pc+o2I15jocZ1Q4ZaL65/+MMf9rVR3V+uD3Gevg/yeTx5yAsuF+FennTRvcLU9ri+dgdvS0/n+UnjcI9MfXh/eF6Xu+43xXXh46gumyvxON1TVt1mrhtluCxq5y4oU/Gq2/F6cNzXegrJh6vHuPA0LpfCcCqzTit/V98hk8uFLgpXmMrVvafzPgCvjzKEZOKK69NzHaAd1pl107/W99j9SDZvWQu3PtcQ3pKXuTsZ1pM8n8cP3vzr46awXFZ9nV0266Z/re+B/+f6cDzRNmyMrrBoGFscNYUQQujnWOx4MaHrv/xfJys5hOMAu6d817OxsbG0j/fDcmCHkm96YGdnZ/xNUFgu2fFa7x2vY3fUGEIIIRxnYnitt+GVo8YQQgghhIGI4RVCCCGEMBAxvEIIIYQQBiKGVwghhBDCQHR+XB9CCCGEEBaDm1r5q8YQQghhQPJXjfmrxhBCCCGEMAAxvEIIIYQQBiKGVwghhHAC+PXXX8uxFo5fJeiD3+dUOtx3333Xxnz7NQOFU96qg5w//fRTe9eNfiqvTx+1B7/CAUqP87ZZFHMbXnWHLQOUnzRols1B6ie9Og1om2UMWgbEMgbCvOgBXyZ1my4LxvS0B3cd8TFM+9BOk5g0NmfJH0I4HMyZfEuE46egeCZreKYfPHgwTre7uzu6du1aieM55fdWCd/a2jrSdXgWZlmDaAP95GCN1rFz584VnfUbxg8fPhy3Dyy6HeY2vPgtNjqE32WTUDWHEZKO53ffjoqD1I9xoN87g2UaRvyW2ocPH9q7w8OgROd5OX/+fOtbDnWbLhqNUa6vXr0q/rAXnm8mJMbIzZs329BumMD6JrdZ8ocQDs/jx49b36is0X2/venh79+/H926dav4L1y4MLpx40bxX7x4cXT16tXiX1WYo6atReiKcdkFBuf29vaedgP/zVmMML2ALoqFHzWyiB9GSDodw+6oOEj9WMmbm5vt3WihhtGyYVAdhL6BvCjqNl0kPkZ54JigQj9MXExOk8BA40eWu5glfwjh8PAcAnMcu1pdKI149uzZ2BDzuOfPn493gE4i2nBAf3a9Ju3I1212WBZqeKEIb7bsIOiYAodfzo0ydjUU3qc04X3xfWUr3OO9XocdD6VRRwjFed2eXrsm03A5vCx2xtgN4Eqc16+wOh9p5J+mp8Lkankph76iz1QmV6XXMZ/KJ3+XzsikPKTVPdeu9EqreN2DxgTtIiSTl+Vjh3JAbclVcTWkrceoUJtLb6jrweleMuInr+PpQH7pQHrl8bRdbc6VuhSGU3/VeFkqv25jpVFdisNJJ9XVh9oKR3mOt1kfdbuGEBYDcwNzHDvQPr/1URsVmj85cfA596Tx9u3bsuOlY1fWBM2JDunu3r3b3i2IpsI9dATtY2tr6+vGxkZ7t5fmzXZP3Pnz51vftziV37wdf93c3Cx+II/uKV9+T1PTVzZXnOIpm/gar4d4pSec/ITt7u6Oy+3zA+WQTxBHmtqPLGo/whXnsrifdpJc+Emv9lX+Lj1dHsKUp8bz1HXJT1nISDrqA9dfsgrVRXhfvcoLyClZKZe6gfyk497lcX0kxx/+8IdyxSmO9CrLqdsDP/mko+K66pF8nl+y15Df2wZ5yA/EgesF1EGYdFE6UP1A/V31us7uJ62X5eGS0f2qX3hb9Pklt+QiXOX15fF2DWFd8GdrWVCHzz9d8CziuuA5XtazuUj9mef6dBBd8wxzkOfz+U8wp00rexZqfZf6V41Yzr4TwDFeo2wJZxuTj/gERz6vX79u776B1V2fvYpJZTeNW8L8yO/Tp0+t7zfevHkzPtsmv6dvOqCECd4ceDNo2qy8oTed3cZMBnmA9OiDVf3x48dyhkxY07HjNw69nZw5c2bPm4r0qY9sJul59uzZUo+Y5fiT8kmH1e/f69BP0hf9Hd6IvB8BfdCVtwQ/K3foK+10IOfTp0+L/8WLF+Ntb2gehvG99OUNRDtWkusv/aW/VNqSe6/z8+fPrW8ytKv6QW3VVc+XL1/G8qhv++B7CX2npv5EP1A/vXv3ruhFHThAZumqMdg3jmqQ/dSpUyWNygC+10Af8LHFUQLPGG/J/m2j561BJtqYvq+/3UI+HU/wJlk/09DXriGExeFrRR88i77OOTzHej5PIsz3XXaBYJ7kU5y+9jkMSzW8YNKCP0lpFiw6fdLiNosxMY1ZF2bBYsFgnbQw1aAHBotcnzEp1NHUhQGEQTEvPDQsepTB4ti1ANYw0EgPXifyIDd6E+8LN+VqQRfoR3q2cd04dm7fvl3yUZYMt2mGjINB5m3qxtoi6asH+Xl5wHi8fv16CavhwdbLAAYX3xJgYKIzxrXAyPc6+h70WcYRBhTtTrxPmsitMcDHtNID+elTjCR0nQX0IQ/9Nm1y7+v/ofovhHWF+UYbC334XF7Dc87zqxfSkwZt4y+bzM3eXsyjeolnnpxnfZrGUg0vJlMMFO1sAMYS4UzarjSLwZ07d9q7b4sRafv+UmpS2bPCX2z4B4hd57sOdbGQTjOcHMnpZU87N6ceFvbDLErUx4OnMmZ5eDAOaPf6g0rkZdChN/H0lZDx6/2gxVZxXQ+3doOoE0ODvmf3ZxY0dlQudS/yoRCT6kFmDBnaeFLbMsYw0ED9iM7oD+xE0Q4ql6u3pZhlHCEnO0lq9xrauB7jyI/xNM8YQx8Mpz4DUfC2yDiuGar/QlgneIZ4IZJjbdNzrTh/znju6udT39PieH77TixWBdYa5kU2FzRv1royz+gllKvmHdqGecz1JUz5KVdx3pYLoVmU99ARtIfG8Chp5Go4E1Uc/kb43vSN0uPwZkEvYc0iMA4j3uur6Stb900jl3J1T9k1Xj5p6/rlx4H8lK2rpyO/4nBQy8m911u3g8sgRxpvW9erS89adoXXKB9luJzSATlJI3kJB0/n8uJ3WfD3obJBdXMFl9/9qt/DKMdl517y4mg3x9tReuLqMqErTBCHm4Tkkgykr8vx9utqX+E64tRWjust3ZSulgXqurm6zoTV466r/dSPnldtU+cHT1e3RwgnHcb9OrNu+tf65keyVxAsd3ZEfCeFnYp6J2oSXennLSNMhvbkmPGkbsWHEJbDuq+z66Z/re/Sv/EK81N/sKyt0VkhvR/jAtun/l1RODx83B6jK4QQwjxkx2sFYcfLja/z58/3frfTB7sxbnxtbGys/Hn9ccC/F9jd3Y3hFUKYm+x4rfeOVwyvEEIIYUBieK234ZWjxhBCCCGEgYjhFUIIIYQwEDG8QgghhBAGovMbrxBCCCGEsBjc1MrH9SGEEMKA5OP6fFwfQgghhBAGIIZXCCGEEMJAxPAKIYQQTgD8B88ca+HqH9F3/MewcfzYdA3/CTdu1UF+/UB2H/rh6/pXYGgjtUEdR5soTj+4vSjmNrzqDhsCGuCgimsg1o26CJCpa8AumsPoPwTIN23gwzL6gYlh0gRzUCizq1yNp6E5iJ7IucrjZl5mHWchrCvMEXxLhHvy5Enn888c9uDBg3E6foHj2rVrbew3SFP/7NwqMstcTBtcvny5vfsN5pIrV66UNtja2tozv+KnTYjb3t4e3b59u41ZDHMbXvzsDELyEzQItWww9BgYB6UeUIuCgdnVmcuAnwu6dOlSe7d6IN+NGzfau26WYaDy4Mw6OcxjtFAuk1YX+rmgIZlHT8Fzs0ow+R3GaKL/ZpkHqOMkGZshzMPjx49b37efietbNzz8/fv3o1u3brV333j06NFoc3OzvVtdsEGmzcno2jV3XLhwYbxuXbx4cXT16tXiF2fPni3X06dPl+siWfmjRgy9wyx2r1+/bn2Lhd/o29nZae/CNOb9rclZ4KHhJWAaLMbz7LRRbt+kc5iXgIMyq57Oqv0u58OHD1vfwWBBmWUe4E0+hHVFvx3LnNf3LNS/L/vs2bM9hhh5a0PsJOLt8Pz589G9e/fau1HRn5dd1g3iaKNFslDDizdNtv50pMQuB369feuYEjytwoBw8imtL5j4lV5vz0pXp1U4lvsklBenN2XV4/V52V1wFKS0Qve+2+N6+y4M9+ikOMkC3Kt+9CKd9PMzeG8LXNcuk/KqLqVRHup1vVW+yu5qB8qo5ZXzcODtqi9OqH7aR+lcJmTpQ2mUDz35wfFXr16VMMrpK4v0Cp8F7y/K1HGgxoKX7WnV5l4f+Qn3fGrzLtQ2OG9HhfvYAq+/ztdXllAcKK3GBff4JXtdL6AH7U8/SDe1Ec7bqaarLaGrPbliGLMTLTm8HvKEcNLhWeFZ4zlgXplGbYi9fft2pU9YFonmYE44fO5Cf44YednjOHLh7fG1oiNoH83b99eNjY32bi87Ozt74vATBo0i5QqNQq3vW3ncN5NmqR+nPEAc9+T38M3NzZIXiFOZHs6V8ii7BtkUTrlKJxlUHulcdkEer9ORzEAcTmUL0khuwtVuag9QHHkpQ7KpPZTH/XU9QjriJK/uQXIKL4Py1VYOdZJOunKvdKpP9/i7dKwhHc7b3MuhDO9fydzn97aBrrK4J1zg97YQns7jqYNwXF0efq+ffLqnjTwOv9rS9e/TTW0Mtc6Eqyz8rrPK7ivLqduG/OTDEa6ylU51Ol4n8aoTiPN7QXriQLKpDMaO6unze37JpjYI4ShhLC4b6uh6rhyeET1T4Onx84wvg0XqzzPvOnQx7fnXXObx6K98h22HWt992s/SIAihCa0LV4BGUWdKeBqpHhCkIw+TJn5HZXTloS534PWr4XQvuK/roXxkUx6Brl0NL1mJ12QPyu+ONJRdh6tc/C6j37tf5QBX6eDtpvq9PEEe7zvSSXZk8TbmXvJ16S+olzKor69NYZKOjuQXlM29O8lZywzo52lc576y6nLw1+WCZCNOegkvQ+m4El63H3FC6YD2Uxmex8uWfu7Ir34QhOve48ivsvvKcqSL8PzI5HLW94I8ai/SeNtJ9hrPA6Sp80nmLj2pR/F1uhCOEsbisuFZ0ZzRRx1fPy84nsNFQ7mLop4XutBcUc9tDuUo3ueeWfJOo9Z3Kd94NZ1ZjpTY2udstOtD5Vm2QB2+1er6XquZSNFo7OahacjWd3Aog++X3r1714b8hsulb26aDt0T7ufKh4GtUP6QgG3TZgCVbdJ6C3lerl+/Pnr69GnpqzNnzrShk5m3TZFRR0GTjoPQydvNPyIVOn7i24Zm8W9D9zNLWdNgLLIlPysfP35sffuZ5XmpoX9dh2l9TbkcPdA+4ONu3rIWwadPn1rfweA4RX91RH/2wThw3dblCCUEnvlp32rV67A/K8xLPD+r9r3oMuDIkU8VfO6r5/dFzotLMbw4E6XTmVyZ6DA2+NaCvxwA/pqAbz70PYm+b5mkGOXxga6fwzLx+p95Ko6JWN92saBB/deN1EU65BIsptP+Oq+GMjC83rx5M9ZHZbus+KW3jAv09volK/G02TwdTd38FYYemnn16IL6aTccsk9jnjYlDQ82+MPelZYxhEHn5XrbCsYIRsSkxbWvLAxLDB5NRMjNfZ8hqD8W6It3GPdeNn0l3WHa81LDX9/wDYcgrZ4hfcSusai/vCVcbewTaV9ZXSicMcyHp6qDsS/QE4N9Eujrf6XJuL9z50579xuMJ30gTN30G7L++Z//eZFh2h9sqB7JTV9J5hBOGoxtf4nl2dFcqDgf/zwPi/5vEoZG33UyL2gurnXl+WddAq6aD3h5U1sxP/q8iJ+5jDjysMGzUJqJeA8dQXtgC440cn0Qp606ro3wxS/Y8lcZimsacBxGPeD1eZ5m4doXryOOuhyuhHWhdJ5G98hFPbqXPlDXQd2613GG7j3MdcAJ/K6LQAaFuSxeH/nqcnFqI1G336SyBXkofxKUI/1A5eC83b3+SWUqjcYFeHvjuKc/dI/Mfi/dCPd68XeVBd4GyFe3Hyie8r2+P/mTPxn7vW9w4Gm7dCdc44ur6+551Tcuq8upMOrgqn5Re7gTfWU5Kg+H3+XwOOlQozqkl7dRX50gubniVL7XqTT0o8K5gtdTh4VwVKz7+Fs3/Wt98yPZKwLt3iwcc+1yObLufbeH3YvDHmVS7ufPnyfuoPHWwY7NpJ2mcLTUY6FrvBwEdgvZaV3UkXkI68C6r7Prpn+t78r/P15hNrq2jCd9VzQr/B8msxxbxuhaXTC66rFAv6bPQghheGJ4rQDsGMH59hz6ILDjpI+n5Q7y0bhAJsrgO5kuOE9XPYf9zzHDcmE3im/W1F+T+nUeMOj4DoLvqGb51i2EEMJolKPGEEIIYUDWfZ1dN/1rfbPjFUIIIYQwEDG8QgghhBAGIoZXCCGEEMJAxPAKIYQQQhiIzo/rQwghhBDCYnBTK3/VGEIIIQxI/qoxf9UYQgghhBAGIIZXCCGEEMJAxPAKIYQQThjTvtfWr5Pwe6td8MsUuOPOr7/+WvSUc9BP4UP++sbchtf333/fq8SyYIDoR33nRY3OddEgk37uZ5kcRv/jyGH0nZSXsauHq+47Jp/jNskgc9+kGUJYX6bNC8yF/NSbvjuqjQ7WS34K7CRw7dq10fb2dtF1c3Nz3Dbo/ObNmxK+u7s7unnz5mDr7NyG18uXL0dbW1ujjY2NQT6OY4DQKAeFRl8GDEx+G3EIPnz4sDY/aHyY/p6Wl7HLD37XfaffHByawxhNTBqzyIyuJ+GtNYQwGxgPZ8+ebe/2w5zw6tWrMhfCrVu3Rg8ePCh+8ejRo2KknBROnz5drt4ub9++Hd2+fbv4z507V/R9/vx5uV82K3/UyGJ5mB+P5seBlwEdtbOz096FRXGY/p41b913/Ij00JPMYY0hJs1ZZGYCDSGsDxgP169fb+/28/79+z3z5KlTp/a8sPJShzF2Urhz587YwHr69Ono8ePHxY8B+unTp+KHScbqolmo4YWlrSNIlOIoBz87EcBVx5OeVmGgIyClpRyBX+m1Nap0dVqFT1t4lBenbUbV4/V52V34WbHQvR9pud6+48E9OilOsgD3qh+9SCf9fAH3tsB5vUJ5VZfSKA/1ut4qX2V7O3g68imNykQ/5fG0CgPyzdPfDvlwxHl7QVde0tbpuiAN+dC9a1eKcoinDvW7b1+ji3QnbQ152MZnx4o0oDrl+vB0Tld+ZKAO6pIc6necj50QwvGHZ/ru3bvtXT9d85JgJ+gknbDwYs3JF3Oeb8RcvXq1GGJHwteKjqB9bG1tfd3Y2Gjv9rKzs7MnDj9hsL29Xa7QWNyt71t53DdWd6kfpzxAHPfk9/Dmjb/kBeJUpodzpTzKrkE2hVOu0kkGlUc6l12Qx+t0JDMQh1PZgjSSm3C1m9oDFEdeypBsag/lcX9dj5COOMmre5CcwsugfLWVQ5hkBfKrrahPeTwN8ZQtOXFqKyCt9PNwhzjJ5Pn78tI2no5rLbfGTFc/1Eh+ULtRpvShfiC/ynUI87b2evrqla4Cv8pAZtXpfter1pn83kYhhGHw53hRMD/oWa/nCoe5QXMceFqfk3zuWDTL0L8P5jh0xtVzHvMhYXKaNxdNre/CjxqxlDk/1s4F3yfp3FTberx1+7dXWKSiaZSyDVpb3JRRW+JYr7zNY8nyYVwzgEo4b/naau3bckU+ZOPYCSi3GWhlG1blEC98S7KGHQ7fmqVsyuA7ImRDHsIoG7TjQBrKVT3aAqU9iJOMgvjmgSkfCqodFM8ZtqcF9YEgD3kpQ3UBbQ71Vmvz0I13RZBTbeUoTDtJ1Pns2bPif/fuXYknzt+wOCZDhi9fvszV386FCxdKn7Oj2YzjPem68s5zhMmbEP1z8eLFfW06CW9ffT/BB6x8wDmJvueh7r8XL17sOV50v75fQ26evy7oD8aVxh98/vy5XEMIxxvmB62Hmuvwa24WzJ0+RzAPM2eBduFx2i1nfTvOsA6jM/Mj87OOHYH5nfVD65Dm7WWzlG+8ZMCwoLAI04E19aIyDYysru+1aDAaTm4eZGAdBsqg81jUalwuFkZggHu4G52HgUVf26kMIAZYl6E0DxitbMXSV2fOnGlD98MZOvrzgPORphveYh4DBvr6W6Ab7XflypWiM2NNTMs7DfqEsjGaKHsI5n0eanS0idyaRLvg2dTYww010YQQlovmLZzWNvz1yytzJ3OE5kzmORkjyo9jruDlW2vXcUabHlAfs9IOGGfzrlGHYSmGF4shBhe7JHQ6nczOCTsIIIvbd0lojEmGAuUxQPybGwwNt14Vh+Ghb7vU4L6jANRFOv/OhcV63oWIMugwdjWkj8p2WfFLbw149Pb6JSvxtNmk9qihbnas9NAsYkGlftoNh+x90K8YaBhf9DcPLG9fMtYIYyJw44g2qycEp6u/Hcqi7fQWww6XmJZ3Grzh0TdMOPRD/cYoeFMExg0vF9LP3yaRxcdoF7M+D7Sndk/B6+U67UWCfiKd6uHqfRJCOJnwrPNipmefuY0TA8KYZ07SC1itK5sz0pWrDEnWB8KYo1kzB6WpcA8dQXtoFqKSRq4P4poFsfi5NoZI8YumMcZlKK5ZOMZh1ANen+dpFvd98fX5Nk7xhHWhdJ5G98hFPbqXPlDXQd26R07QvYe5DjiB33URyKAwl8XrI19dLk5tJOr2m1S2IA/lT4OylE/1ON5eiqvbELwNankd+sLTUtakvB73hz/8Yewn3NuScr1tiO/C24s0kq+Wq5ZbkE5pkN3lpe4++ur1cMlOma4beL2qR3VzDSEsHz2P68q66V/rmx/JXhFo92YBnmuXy5F177tI7Agd9iiTcvkOKEdSs8EOEm9QJ2F7PoSwHNZ9nV03/Wt9V/7/8Qqz0XWc9fHjx9Z3cPhQPUZXCCGEsBhieK0A+tjv/PmD/cehwC6L/opSzv9ycV6QiTL4Xi/MBruDfEPA91oH/b4shBDCySZHjSGEEMKArPs6u2761/pmxyuEEEIIYSBieIUQQgghDEQMrxBCCCGEgej8xiuEEEIIISwGN7XycX0IIYQwIPm4Ph/XhxBCCCGEAYjhFUIIIYQwEDG8QgghhBMAP6DPsRZulv/EWT/I7/BTc/OUcdQg57Qf+9cPZ6NvDTp+//337d1v6D8Rxy2auQ0vBJQwyxDouECn6PcRHQ38VQEZ6wdrKHgYugb0NA6ab158khqivqNmUrsy2eZ/2w/heMMzzLdEuCdPnnSuUU79zJP+/v37c5VxlMyy1iI/v+rSBfqjYw3z5MOHD0sbbG1tLX4NbQreQ0fQPhpBvm5sbLR3YWdn5+v29nbx036ztOEy2dzcbH1HB21CO8w6To5C5vPnzxc5d3d3i6xcTyqT+oOxS9wqjJsQ1oFlrRE+h02be3nuWcuZBwXzhN9rjlw0i9QfGbX+9jFpju+yZ2r5uD9MO9Tl5ahxAWAZi6ZjW9/RwK5G13bq0Fy6dGnUPAzt3WTYbTkK6KtTp06Nzp07x1NRrieVSf3Bj6DzVhdCON5oDmMdePDgQfH38fbt29HFixfbu28wTwBzMjtF165dG4etC1o/fR1tjLvWtxiWYnixTacjHG1TclWYbw+y1adjDsK5elpn0pmr8ugoRWlVP+EMRuXvWuwlNzJ4WspQ+cqHn44hDz+KzI8jq25wHfoMIemMoz7wttD2JmF1OlAYjvqI0480E0a9hNVtonvVozaa1L7gbUBe4Xp4uOPlKj3l4NjaZrtX8bOWj19p6zju0ZP4uv25V108UHV71GUR31UHqCxd5Rd1myIPfsI9D+GAX/I4pPcxqXGg/lU9ClM6wmukS1eckJy4ui6Vr/xKp3EE3JO2q/1DCMuD5451gOO1vmePOeDx48ft3V4+fPhQ5mQ2FPrSnGQwXlkXXrx40YYsgW8bX7/REbSPSUeNHFcQD56Oq7YD5See+nDc18chvoXo+fvqJ97DKYsykUn1gOrp2nYkv7YUuXp5qh+5PL/LRhhxyudxDjroaId46epyAmEqS2Vz9fx1WUpftyfgd72lq8vZ174KIx55gTzcC8J13yW7IFzpuLr8pPP7rvJxpMORBj0kE3HcS3/X1/G4vnqQg3Rd7QGSQXWTjrJqP2WpDPxeF/mF0jvk8zpIw730w0kPri4r8ute+VSHdATpCsQrj/rt559/HteldLoHwhROWbO0fwjrip6bZeLPqsNzqTkAv+YVoTzMAXXcolik/sgoffrQPNY1FzFf+ZwJPrfKHYY6/8J3vF6/fj26fv168d+7d2/08uXL4ufKkQZvwuzIAPFNJ5djDuKE8vgbuXaVyI81jlU+K1jtTcOOj1rYOuX+/fv35d65evXq6Pnz58X/7t27Ug9vDbzNnz59uoTPUrd0gE+fPrW+33jz5s3o1q1bxY/ulKkjH9pEsB2sHaxmgJWwL1++lLZDL95uaI8uuo6X0E96s3OhbeRZ2pe8xLM9rfhnz57t2a7mHt0OitpA9JXfNXaah6pcSc/bHv3XjPGZjhD76tHYuX37dhuzF9Xp7aX+7mtT6nn69GnxM67oV+0qdY0VxhJpkAnQV2OB/kU+6chbGv0kkF/PG5BW7cUbbVdfdY05jmRVl78FNxNUuZ49e7Zc4SDtH0JYLPXcL1jfNC/xnDKH4WedYx7SLhnzDmuwduPXCdZF5i6c1plFspSjRgyDGh1voAiT90FggKgxugyDRYDRiPEoOONmMWMRWfRZ9+fPn1vfZOh06Y1DDh33cI4/z6BAPy3gNdPaF2OHOBZs6haz6nFQ5i1fDw0gpx+BTWIZenS1qcYRExzjivakT5DTjb+D8vHjx9Y3nT6jqGvMzcpB2z+EsDiYU/Ry7/DipOeaFydervBrLvAXNThz5kzrWz/Y2ADWvkWycMMLQ8U/Npe1zDc82h04CBhr/n1N/a2N0OKmyd7/jJQ3eWDBY3Dpzd/R4ENuFkEG7mF2cPpgV8I/fux7q7hy5UrZLdFbCAYXuvFQ8UYzz4II0o9ytIMHs7QvgxA5eBMiPXLUemBI9O0OgfSg/dFr2qI8b/lAW6IfDwsGBHmmcZB6pjGpTWXQA+OQ9qDOSf0pPdRmXWkZszxramfS+g6qg75dE3PfmJuVg7R/COFw8IzyoiPH8605QnHTnmPmIuYLlcF60bVOrgrsyGFXsIPHnAO1rsxj2rnnqnkNmKuY65h/ZWSB9GcN8B3+hdFYunvoCNpDs5iUNHJdNMqN45uJt4Q1nTkOU7yHkc7jvR7OW8HLbYyOElajeOVXXu67yuwCWahL4Pf6XA6QHh6O3/XrktflIS1pdK92A28b8oCnVb2kQy+F//73vx/7KV8ob43L3yUvZSiN5ADXU3K7HKrb9cWvtK7Ln/3Zn439iu8qn6uHyY983HtdNc2DOo7DTarHw7rGjOL6+ruvTdU+yALkxfVBOa4T+braGLw9vZ+gS0ZPL729TSmjrsv18jLJh6vbX23ubRDCuqLnYl1ZN/1rfdfmR7KxZrFeV9l6D6EP3uzY5Zx3hzOEsHqc1HV2VtZN/1rf/D9eIYQQQggDsRaGF9/WcIbLOfC0M+4QVg12a/mOYdL/yxNCCOF4sDZHjSGEEMIqkKPGHDWGEEIIIYQBiOEVQgghhDAQMbxCCCGEEAYihlcIIYQQwkB0flwfQgghhBAWg5ta+avGEEIIYUDyV435q8YQQgghhDAAMbxCCCGEEAYihlcIIYRwAuCXLTjWwvGLLdP46aefSlp+C9ZRGbhV/7UXZESPPn788cexLnWboPckPb09F/mrIXMbXvx8iQTBLQMaapZBE5aDBpuYNOgYuBqwDH7Gh6AP6csuyFM/7CGEEA4Ocy7fEuGePHky0WhiruZn9Ej74cOHNvTb+qsycKv8w/zTbBDpjx787BptIiONtnr9+nWJ29raKj/J5tAO58+fH+3s7JQ0586da2MWQFPgHjqC9tEI+XVjY6O928/m5mbrm5/t7e0iw2HKWCbI13REe3fyaQZe6Y9m0LYh3dAmpFvWuAghhJPCLOvsQfB5etJc3LeGkx/ZcKx1y2KR+rNGzSoraxC6d4FMaj+tZ4uiLmvhR41Yk4fZkrtx40axPleVBw8etL71wN+EJsFbUTP427v9HHZchBBCmIx2ZZhvJ61V9+/fL2l1cuVzc2MXlN0hdsP6TiyOM2fOnGl9+1H7PXz4cNQYaeP20S7Zolio4YVwdNarV6/GnYmT8AoTdKrC/YjK0dFmV3xf2Qr3eK/XYbtRadiWRAfd49cZMLLiZ0CyJUk+HZdJRurw8vB3QXqlUxqVoXJAsnBVOvye34/rkEdl4ITk9HihcojTQ6b0zvv378d5iRfc97UtciIvrh4X0kP4WCAOSMc9Mkr/EEII/TCvMt+yTnXNzczfHKFduXKlGFkYGNeuXStxMjy4stZhoJ0kaA82d2poE9/wYa06e/ZsaR82FGjPvnXuQLDt5XQE7WPSUSNbfh7HNqAgTuWzledHT+TRPeXL72lq+srmilM8ZRNf4/X41qL8klHbj0CZhBOmergHhdV+h/II9zYiDFkAOV1uIB6/5MJJH9Iqr7cHYdy7nCrPdZD+KkfpvSzPq3KBK3HkAWRymYmTnB4nPXTvcapfsnElnrAQQjgJDDGfUYfmd8fnW9Cc24Xm4EWzSP1Zh7TOTELrWxfeTl3tQR1a5w9CXd5S/6qx3jnB0mw6vIQ/f/68WNzi8ePH5UM3h90OwruYVHbTcCXMj8k+ffrU+n7jzZs35WM76tGHdVi1OjYjDBn1FuAQ1nREeXPQx4eENW1admwI7wJ9kPP27dttyKjozZsFcmBZS37KI+zu3bujly9fjuUiv6x2tkTRgzr11gL37t1rfaOxnJQhPn/+XMqnbtqRtiKP9KpRP5AG+WinSceQ0rOL+ljy7du3490wtduXL19K2+uedg0hhDAbPsc6p0+fnvkTEmBNOO7oFKVLF3YIWWOF0ix0h6ti6f+dxKQO7jKGBAYRiy6GVB/zDJ4+GJws6nJqdGRrrOBiCM0DxgOGhIynWcHYcTkA4wU/BlV99NfFQQYKbYjxhdzzHOct+mFs3kb26I9xhnGJn/ZEvmU+CCGEcJJ49uzZ6NatW+3db2ijQMbIixcvylpXM2ntPU6gB2uINiN8ncPPt3BazxRHezx69Kj41Q5qt0WwVMMLQTFA1MHAQk84uxl+fsw3RHfu3GnvvilO2vpPPMWksmfl6tWrewwrvjNicVeZGD4YBCz6s0A+dnm0OzQrGFa+A6bOl7ElA1OGB7tDgoeLvBcuXCjhGiSkJf8kA4k0WPukoS3rHccuaKOuh/QwaCxIP9oRPWgHrrQndTJGQggh7Ie5krVKDoNC66HitD4w17P2EcaJidYs/9aW05R517KhYY1j7UIXrduuK/pgQ+hkCyfISzjxipOhit7akCBea/DC+FrREbSHxrAoaeRqOAdVHP6mUXrTc+aq8GZhLWHb7fc8OOK9vpq+snV//vz5Uq7uKbvG4/FLfuoFxeFA8lC2wpUWFKZ4ro7XR13C9URvqGUDdPC0Cgdve9XrbUQ+z/vzzz/vuafsOj14uQoDbwNPg0y1nh7/+9//fuyX/D4WVAdxkk/6qByuIYRwHGEOW2fWTf9a3/xI9jEDq55dLv9eK4QQwvFh3dfZddO/1nfp33iFEEIIIYRvxPA6RnBmzVk233L5B4IhhBBCOB7kqDGEEEIYkBw15qgxhBBCCCEMQAyvEEIIIYSBiOEVQgghhDAQnd94hRBCCCGExeCmVj6uDyGEEAYkH9fn4/oQQgghhDAAMbxCCCGEEAYihlcIIYRwAvj111/LsRZulv9km5+gIy0/GC38h7L1w9OrzDQ5XZ+uNiHs+++/b+/2sqw2mNvwQkApgRsS/ud2HyCLBL3UwBq8q8aqyjUJBjUDfx7oY/2K/irqvMxxeJT4MxBCOH4w3/ItEe7JkyfjebQLnnd+CYW0Hz58KGE8/2/evClhu7u7JX5SGUfNtLVBsksf2sTnONqLsC6Wue7MbXjx48xbW1ujjY2NosyycQv10qVL4wGySKiDn+ER58+fb32rxTS5vK1WAQyuvkHdB5MBD4hYxb5Y1jg8SupnIIRw/Hj8+HHrG5U1mrmqC70M12v427dvR7dv3y7+c+fOjTY3N0fPnz8v96sI8k9aI9D/3r17xS99Pn36VO6B9sKe6WJa2YdhpY8asUzZ8Vg2ND6DVPjCv0pMkmveXaUhYMAz0OcBw94H+6r2xUmjfgZCCMcPjAtg7Xzw4EHxd3H//v2Sll0dnNZZrm6YnD17tvWdHM6cOdP6jo6lGF5+HOnblBzPKNy3+9Txnod4/SC04gmjbFB6z6c0+LX743FAuMJm2SGSLjpaUvldhg5pcKSR3lxVHw4ks8pUHeSDOo/HgcdTFrLwILG7RFgNepJGurve0gdHGm9P6qn1VxlqT4c0ylvHqxyvW2G4WQ1H111lSWaX3etXmJzy1boL7pFN7VFDPuIF6b1uZKwhjPZx+UFtpv5VnGRUe9dlKh35lAYHyO15FOdhknda+bP2SwhhNWBuYu28fPly5/zFs87L7ZUrV8quDi/H165dK3FXr14dPX36tPhPIrTHjRs32rsjpGn4PXQE7WNra+tr83bc3u2l6cQSD56O8J2dneLf3d0t9XDfDIDiV72ef3t7e5yftKTRvfKQHwgnPZCfckB1cZXfw4Xnr+MoS3Eg+RziccorXSUfkE/30l3gVx34ld/bQ2WrDfp0dggnD460Xm8tg+SmHg93/RVf47KQXrJwVbl9dbsf8BMG3hfuB9JRJ2E4lVHLovYjTG1X1ykZScsVR1itq8JVDmWofvD6hOTuykcc6RUOxKleydKF6gTKUL3kVR7K9bJUp+RROwFpuSedh4cQFos/u8uCOjQPOz4PguYD4XMabhlzgdd3WJB3FhmZHzUXOvX868xa9jRqfRe+4/X69evR9evXi5+jJo6OgN0YnTezxdkoO3r37t34W5mmQcoVPn782Pp+g7xNA7R3v6X3b218i7QL6m10LrsETYO2odO5e/fueNuWvNLPuXDhQnnLePToUakDeUmrNwnQWXPXW4jDLsjnz5/bu/3toTaFaTrruI/2lqWvtqP98Wt3A6hX29W+O/Ls2bPiJ4/iHXYmVT7HVv6tAXWr71U39/QdOyq8mc3C+/fvy1XyUhb6TxoLbJV7+ylNn+4XL14s8hBPP9a61uNwlvFLGY0xU8ac9x3lEFdvfT98+LCMI0AOtV1NM1mMd8qoU2+qL168KHnoN+STDoQxFmhHyVu//fGNB/WvxFthCOHA+DzlnD59es9cWUMcc5/mrJMwF2ie7Fq7joKlHDV++fKl9e1lmsExBCyyLC6+UE6DztIxEQt6V+cRxmBl+5Y61NEH0RmjBSOOcjBk3YhZNCzEyC2nh+zOnTtl0UdnjE4Mq2m6zKMraWXw8IDPCsaGyytjtg/iaUPqok3xiy7dMU7wA3nqI7ghQI5Z/iiBj2AZy7Ql4w5qeecZ50D75CP7EI4/vCzfunWrvfsNvchpjeJFjbnQIY4X0EkG2nGBOZF5UmuFPuM4ShZueLHDwxuz0DciLJh6iwf+ZLVr52hRyAjQLglyMZiQ4yCGDMaH/tqjC8pGVxZN3jToaHbBWMS0GCITBpwMNy2KhOPHMMDPwJAxsMyBz+4OC7zk46qHkTh2ULTjwoPJA9r3YWLdv9O+DdLDPs1wctSekpG2mlYP8UxAak+1fZ/upOeKXNqVPQqom7GCnH3QHuhAWzLuZCwLdOWN1dsIw0rGdRc8uxjC/g1bCGH1YQ7jZVGONUtGluI03zEP6OWetVhror751Jy56jBHau3UuuC6MvdhQDJPEoZziOf7aNaVes7rKnthNI27h46gPTQLbEkj1wXnoopvFpA2dG8435HUYaR1P2l0//vf/37sbxbssZ/8ft8YPXu+YZG8hIHCVW+d3+skzuFe5dRQr7eN0k0rT+E4yoC6jXGuE2lrnXG6dxnrNpVfsng+hQnu1X/SYxIqB0e5k+qu9eGK3nUbehrw9sSBp6nbxev1cOjSnfQuQ43XT12SHVfrK1yPWkeXF78gj2SaBGUpn+rh6qh8j9O96nA5XEfK1z3XEMLh4XlaZ9ZN/1rf/Ej2HPA2sMxjP4EV7jtB7OywczdppyJ0U7cldIWtGvQ5O1mrLmcIYX7WfZ1dN/1rfVf6//FaJdi21Hc0y4QtTbZ+HRZgjpXCfGC8sI3s0I+r8P+4TIM+X+ZRfAghhKMhhtcU2B3BWh3qL71UB3XKQdcH/WEytNnW1taetlz1v9jjO4P0eQghnFxy1BhCCCEMyLqvs+umf61vdrxCCCGEEAYihlcIIYQQwkDE8AohhBBCGIgYXiGEEEIIA9H5cX0IIYQQQlgMbmrlrxpDCCGEAclfNeavGkMIIYQQwgDE8AohhBBCGIgYXiGEEMIJgJ9J41gLx28LT4KfTyMdebrgV1twqw468FN7fUhPXJc+isORVnz33Xed4YtgbsNLP2kit2homGkDZhLkXYXBMqsetGHfwF8GyNQn17xtR1qNg76BqfHCID4smlRWGfSt23AVxuNBWcTzRJtMmhhDCIuB55VviXBPnjzpnZcJv3z5cnu3n67fuV1FZlkP+Jk42mN3d7fo5Ostc5vaC3fp0qUSTjteu3athG1vb49u375dwhdGU/AeOoL2sbW19XVjY6O9OxxNY5TyFgHlIP+iypuHnZ2dr00HtXfDsbm52fqmg3y0zzx5poHefWVOipuVRcq6bPrG31GMx0XQpc8s/cE4o+9DCN3wXC0D1lMxbY0mLXJ4HsFzjlvW3LVI/c+fP9+79ta6eZtIf1yd33VnLqOOw1Dre+RHjY8ePWp9h+fevXujpsHau2HBqh6aeXci+HHoZbQPZfJ2Ve/cPX/+/FD1sUsy5G7gYWH8NQ92e/cN3iwvXrzY3h0v6udp1v548OBB6wshDIl+WJ9n9aDPIXlv3brV3h1v1B7Aevn48eP27huNDVR2wm7evLlnPUV/7Y6xjj179qyNWQwLN7wQVMdPOJ+o/cyUcLbzWLBRUEdROtrwctQgyg8ez1FGDYNH8dpuxd915EX51KtjMS+PcJWDH1S3x7169ap0nvJKD6GycZJH5Qhkc7nxg+uK456yaTfajzCgXKWRrODhk0BG1ak24Uo+b5OaK1euFINjFiPa9fP2UT040uBoT9qVMHRGD+8/hSuf2hUUJqf2UD+QVro63lY4obbg6uWBdOoaW+/evRtvX/eVLb0kGzrVdOXrGheg8rw+oXYmDh0ku/RR+lqGrv5QXpx058pExjGGypQsgnvl8z5QuYpTHumh9g8h9MM8wrPKM1g/x7Pw9u3b8Zx1UmDOYb08f/58G/KbUcaVOYt4gf7b29slPevbwtvj28bXb3QE7YMtuL5tzOYNebxF537SazuPcJz8SsOV+nUPvsXnxxek0zYiZXfVSXqX08sVyERZOJWPn3BtRYL7ld63J12/Wo9aR8mkcoAw/NJXcoHnr8viHlw+oJxaB8CvPA5hxJFHdeOooy7Doc1Iz9XTKB/lqj7SeH+SXvnVJqqLa1e48nPlXnVSR1cbeRnUpXDyEVdDWoXLTx7Vxb3r4X7JqDpAuoPrThrulQdHWV2QTnHIpPK9POSiDC9Petf5dZVslCe/8nOFvrYEL7fPT3rXjfLkV13ck0dyg9erOtVmIRx3NM6XCXXoue6iftbB0/szuGgWqT9zgubsSUjfPp3Qt26LaXlmpdZ34TtebOVxRIGFyW6M4E2Zoy4gTb3lB11HhZQjq/3z58/lqjfhpsHLmzBlf/z4sYQ5vtPQBzI1jVrqVXrusfqxhJs2Km/l1CWazihX6VNT6/H69evR9evXi5+4ly9fFr/KAcKoQ1ualM09sve1qfP+/fty1W4BZX/69Gn04sWLPbLU7Suoo1ngit/bhHrFpLcn2g55tYtFf/g2L7D7g1ySEehT2lo7KWrnL1++lKugrGaBbu9Gow8fPpSrt6HGwNmzZ/eMB6VFRt5qeCOkX7v6j34gHFmQCdSftInyqF62oe/cuVP8yKg2BPpOW/aMIT7WFN6u6IXeXW9VanPFIR95KZvxIJCLumk3ladxBnp2AN2I63oG54F2PXXqVCnP+8HRuBaMX+lCe9GmjItJ/Xn16tVSB0e2ShdCmExjjLS+2dEJCg6/5suTgOabSWjNQmd2urhnTqIdDrJ72MfCDS8dZWBs1Iv8QQTnrwkwHmqYzFk85foWEfKzOLLwyfiZB3TBMOhbWGalNiTmYVKbOiy83ia+uA8B37kxQN3gqEF+l1GGDA+Eh3cZIbOC3rQVbcaWO35B2Xz7QJyMRIejLOJI50bUQfBjRjjog9s39g5ihGAIoRs6HnZCJb/+8seNq2nM2w70J3UwvpA7hDAdXuLn/VaL50xOL5v+Anfcefr0aacd0LU5w7rvyChbBAs3vFCMN+5aSBYx/waoa9HrgoWZxRzDSYs0ixmLkZfR9+0HeVh4GYSTGs4XZ3RgwFInch92Z4DFyT++n6Q7izVoIKBrX5s6Fy5cKDs0yAwsbtRz5syZ8uaixQ49uVe6RUJbswBj7HYZTuxYULd044ocvFn4GwVhXQ/CrKA3/a0JRO1GmYwTZKM937x5U8Id5JvHyGZ3DdkFfSDj01H/KBxd2bGa9jATT5v6mMGvZ8D7EUOsq90d7ZLRLtrRA7W9dk59d64L0pN/XuOvngfog2kvRBh41McCQP7DjI0QTio8F7yYyPGCqflAcT7/6GWJq57/4wbzGfMgL9iaC11XwrxNWP805zKPKpz12dd55hrWAuJoH9aLhdJMwHvoCNpDM/GVNHI1jYU8jmsELledvyrcwzy9+/3MtrG893xbAk1jj9PiuO/LTziuD+JcL0+rMOmiq/wCGRXWJYfnU/m6VzlcXQ50Ai9P5VAuztM2g2N8jxOSDUf53Nd4mj/90z8d+71unONxKpMw6ec6S0eXWWHgZamvXZ+ff/55T7yX7XlVv+7lqJfyvH25r/F2UB0e5mVLfi8TP2lUn+P6KK+PY+ldU491lVuH12GUV+vr98jZlYcrYa6r9NF9XZbainwK5+ppiAPvO+niYV4vftpf8ZQHGkcqM4TjBGN3nVk3/Wt91+JHsrFseavu210gnu9JDruzdViw3tmpmbZrESZDf9bHrF1hIYRwFJzEdXYe1k3/Wt8j/3+8hqDrQ+9wMmHL3I/+gC1njlxDCCGEo+bEGl4swFiZuLt377ah++EMmIWa81x2RY4KvmPZ3f32/x8d1/P2VQADe2tra9z3OM7v9X1gCCGEcJSsxVFjCCGEsCqs+zq7bvrX+q7FUWMIIYQQwioQwyuEEEIIYSBieIUQQgghDETnN14hhBBCCGExuKmVj+tDCCGEAcnH9fm4PoQQQgghDEAMrxBCCCGEgYjhFUIIIZwA/D8O/+GHH9rQfvQj0vrxfMCvMvSj2seZ+oeyXSf+03SF60e2h+BAhpd3bu28Aw+Kfl0cN+l/ca/loBH5H+CPM67Tsge9t/NJeMBCCGGdwdjiWyIcv8YyaV5nrbx582ZJ++HDhxJG/tevX5cwfgGEX1I5zrCeouPm5mbRievt27dLnH61hl+MQVfSDbUOHsjwev/+/Wh7e7s4QGiU2tnZGd25c6eETWLaT/PwI9E00Pnz53t/Y5EBQrwaFAcaQIfhsD8ddJj8+skbWOaPZTPAeKjow42NjdHnz5/bmBBCCMeRx48ft75Rmdf71hCtUVo3Bfm15t67d69cJ21+rDpfvnwpV+lw9uzZcoVnz561vtHo+vXr5fr8+fNyXTpNw++hI2gfjYVYro3RU9I3Ble5nwXy4KbRGFW96VRvV/wsZU+CehtjpL2bn2awf20Mp/buYFAGbpmg57LrCCGEsJ9Z1tnDwBo2aV3W+skVpzW9ZllyLlt/h7WO+nRVu7D+SXcc/mWtibW++7SvE0xCgneB8UEcTsaQ7nFSUJ3PvStNWJcBpAbCTUPpcBpYnlfyE0dnKA4n48nDkdX18vj79++Pw3EqG6eOxu9GmcoiLYNC4Pd0QgOHOPlrQ9PrlV4qW3kVj5Ns4HnV9mpvoBzqU5+RRuVRjtK6TApT2apP8hOv8lwWUBqlEx4eQgjHjWXOXfUaUKM1QXM88y/3NaTrWocWwdBzt68ZWme0dtEOXWvXIqn13ad9nWASpO3qMC2kKCPl8KOg/OBxlCOlaRjC64UYlGdSA9WN6H7qYWByrwEqeQhzfahL9y6f9OBKGUKyCb+XThrsKoNwylA5kl3pHMUhC37kUfmgfCrb20G6ery3L2VKV9K63tyrLnRSfrWPypVfugD35HWZQHVQhmRzeZRPOhDn/rqtQwjhuDDE3KU5tIa52Odozas1XXkXxZBzN+sMa4bWIK9ba5YcaZaB1wn7tK8T9OGLuoOChLMwAvEq0/3g+fFL6UmLquJUfhdayEFyKj1+Gpu6qBu/wK8BKT24Sm7KEtzjHBkTwu8lt8rwuvBLPg0Or0vUcS4/ZeGkr9qVMPw+oGpZdI+u4LKRFyf5QPUqjrxet9J6PfglK+DHEa50QuXXqM56jIUQwnHC57tloTWuhvnT51fN247m82UxhP6gNVOgF/e1boR3rTmLotZ3n/azNogU8gUdtBAL/FrEUUx+5WcQaCHVwk8ZSlejRbpv0e1b/L0eDUaXp84nPXBdHVLrCV42+L3KAslBG6hetSP19w0AyvI4la8y5LxPuK/bknsvx9tBsqkdiKvl4Z40oLKQwfsUvFyu8ktej/M6vH4hueRURwghHDeYw5YN82rfPMl862uOr1v4PZ/HLYpl6o8+lI9+XWuSrzVAPI51aVnU+h74//F6+/ZtuV64cKFcnUaxctX/I8JfSvBXBY1io6tXr5YwcerUqfGfd+ovMPhz1jqd0F9a8GegDn+l4f9vyZkzZ0qdpGsGTin73bt3Je7u3bv75OEvNeHixYvlCujRtFH5S0n/bzL8//vAT736M9QrV66Uq/8VBX+22zwEpTzS6i8tTp8+PZZZ7fjmzZvRtWvXir+GMiWHt61oBlmRF2gPyfTgwYNyFa9evdpXh/6Shb5ATtpZbfTw4cMSJwhrBnfxqyzya0z4X9IQThuRrm5rydUlz8ePH8sVHbxfmweo6EhfDvn/roQQwirDXKn/HgjH/Kq5WHFaE1hj+e8TCGPN0TrC+sJ/Q8FfvKucW7dulbjjyI0bN8r6L31Yu/Q/H+j/K2MtY03RGjgITYV76AjaQyP42EKUI0xgWSrcLcs63O+xqOWXhSpX73wIT1Onw9JXuKx6UD3gepBeVjKOdC4fjvQql3hAD+7dssbpTUP3+D2t1+26I4PSKc4hzHVzPFz5pJOjur1dat2FdOrqX9dDZbkuhHl7KRz9VJ/K9ThweShDeNuQJoQQjiPMYevMuulf65sfyT4m8KaC1d4YK4eyzNkl4k3nsOWEEEI4GOu+zq6b/rW+cxtexIewKqzTwxtCOBnE8Fpvw2vub7zIHDes29zcbFv/2/9G3JVmmiPfYfKvqgshhBCOEzlqDCGEEAYkO17Z8QohhBBCCAMQwyuEEEIIYSBieIUQQgghDEQMrxBCCCGEgej8uD6EEEIIISwGN7XyV40hhBDCgOSvGvNXjSGEEEIIYQBieIUQQgghDEQMrxBCCCGEgTiU4cW5pdz3339fwn744YdyXSd+/fXXcTssWn/Ko9xVxXXnB7gnIV2+++67NmQ6lKnyqWsR8IPjs8gbQgghLJoDGV5auIAPxnAPHjxY6OI4iR9//LH1rQbnzp0b7ezstHeHZ9X0mwS6b21ttXeTefz4ceubnRs3box/ZzKEEEI47hzI8Lp8+XK57u7ulitcunRpkL9S0M7aSQWj682bN+3dN2Nlnf76Ywg0VjHqQgghhCGZ2/DS8cz58+fLbkfNy5cvW9+oHCnpmEj5/LhJO2c4/H6PASK/dtHI8+rVq9H9+/fHBpgfRWmnyPOqTOqVPC6X8ng5yqM6vDw3/PyY7fnz523oN1QH9Xp+4fWRFkiLbuhIuKf58z//87EfR93Igh95wfWq8bLIp7w46Ss5vB9wQnm4SifKdVzXOs6RrJTlKK9kcV68eDGOF94HOPWntwVySC6XT2nrMjTePK2PoxBCCOHANG/+e+gI2sPW1lZJs7Gx0YZ0s7m5OS5LeXZ3d8s9/sZwK36l29nZ2XNP+YThJz95VQ5XIIz77e3tsV91UL7KoUycypPsqkt5COeevNThMngdqp90tR5cQfl0L3nAy6Fc/KSXX/KBZCKP8imePJKdelwW1evU7YcfB9QtCHO5VZ/qVxnEkU/lyi+ZulB+0khfyUM9qourh5MOmZRH8ioOkEdtAPgVB8pT1+s6cu0qg/C+dg0hhHnweWkdWTf9a33n3vE6c+ZMuX748KFc+3jy5EnZFQPlYcdiVvhmzGF3TeUIlXfz5s1xXe/fvy9XwQ4cx3X+fZF26q5cuVKudZ7Xr1+P7t27V/JqJ+v06dPjfE+fPi27Io3xMN6ZuXXrVrnOguRGH467mn4ox18XLlwo4X1Qf2MAlB0x6v/8+fNYJtobediVwY8ONegEHGWyC9QYHuWe3ZxPnz4Vv3apzp49W67op/oEMlAWY8CP6549e1Z27L58+dKGdENfIbf0/fjxYymferTbx9WPXOHu3but7zfoI9oPOdEfJ+7cuVOu6ITrOlpEd/KoHblyT7jTNY5CCCGEeZnb8NJiyeLki7GoF6xFwMI8ie3t7bL44k76dzu3b98u19pYBAwatUOfYSzDDS5evFiuGJe1UXsQMFqQQTIeBOSTDn5sXeOGIoYahqaMb3H9+vVyffv2bbmGEEIIR83chheLq3ZKrl27Vq7Cv9fZ3Nwc7z5okdRC6HTtzMyKDAd2WkA7G9OQwagFedJOk3ay2F1SPnZSZGTIiCG+D/L5Tozk1q4exuoscoMMS3b53MjEYPGdmvrbKSGjiD5hlw0d2CFTWbrK2MWAo2ztCE2CHUTSI0df/Y6MR9rY21PtPEsZtKEMLm9joExkRz92LLtQG6hOrtwTHkIIISycrxUdQZ00i1xJ645vjpxmARvH6fsa4NsahTcLY7mSVt9F4fiWRnE4le3x4GVNCgOV73KRFvTdD456nVpe4W3gZUpW103xkqevPs8zKY3rJZQWh2xdSGbFU46XDd4POCHZcOrPehx4XnSooQzXS+VAXRZ4WvrB25n0kolw+b1tlMdRfpVR18s99I2jEEI4DMwp68y66V/ru1Y/ks1uEP8VRrOI5ludEEIIR8JJXmdnYd30r/U90P/jFUIIIYQQ5metdrz0l2+wtbU1/iu/EEIIYSiy47XeO15rZXiFEEIIR00Mr/U2vHLUGEIIIYQwEDG8QgghhBAGIoZXCCGEEMJAdH7jFUIIIYQQFoObWvm4PoQQQhiQfFyfj+tDCCGEEMIAxPAKIYQQQhiIGF4hhBDCCeCnn34qx1q477//vg3thp/QI92vv/7ahuyF/MT/+OOPbchqgozoPYk+XdGNcNwPP/zQhn6D/3BdceRfJHMbXuoMuVUBuaY1Po2+KJnVkfNARy66Aw8Lg23VHyxnkX3oLKsdGJd95c4yZtcF2oH2CCEcDObGt2/flm+JcK9eveqde1iH+N3iLjTHnjt3rpSzyr/wMsta0Kcr4ffv3x+315MnT8brM+vBtWvXSvj29vbo9u3bJXxRzG14vXz5svzczsbGRhFqFaCRGGTT0EBaBH2Dtg8WFf1c0aKoLfSDwI+FH6efTjp//nzrWxxMTjx0szBPm1PupHHJs3Tjxo32bvVgEhrCMKSemzdvtnchhIPA+sZ8LlinP3782N7t5dKlS73rEQYHxoaXtaqwnk9bEybp6nnrcs6ePVuup0+fLtdFciKOGhkgGIJDsrOz0/pmg0V2kUYDi9Xr16/bu/Vh0cYrYHhubm62d/3MuyNGuUOPy0Xy8OHD1rdcmBiZ6EMIi0XGw6zoRevZs2dlN+kk70Iz7wDzOuspBqfCbt26VXbD2P17/vx5aY9FshTDi86i03DaugM/M/U3ae61vak87Czg9x0GHc0o3aRtVKXBCcKRQRBX1ytcVkfhdEYfyKW89cD9/PnzOM7bwNtMeqkNSCeddFzJjhtGCGEuN0hP5cGBZK/r1T1Xb2PSCy9L5YHqkvy0p1A7EO/tLP3wq3362oxwtYPLA5IT5/WKLtlcj77dK5dV+ZCDB5GdMcKgr6yu9uuCeMoA6c+9ylI5XAlTfWof8isPuNzehtKfdJRVo3LlgDzs1rET5WWB+oNwz0vZkkE6KK2HAXkV5+Gg8GntF0Lo582bN3OfZnBUiQHy4MGDsps06bjyJPDhw4cyr/OS6Tt8ehlks+TKlStjg2xhNI27h46gfWxtbX1t3uTbu71sbm6WePB0hO/s7BR/YzCUerhvFCt+1Us6/I3SJb4O173iKAuohzx9ftVJfdBVr+T2/K6Dh0ueLlwu91On6qcc5acOlQuE697r5Kr8arsa6YmT3NKVuLpPVJfaE0c4KA94XZTBvddF/i5IixOkFdKLvKoTkI974lW+UJ3geWq6ZPO8gA6SgbIkZ5+fq+rsK8v7RWlcf4GOLpv6RLrLD9zLTx3yg5eBnzqBNNRLnOonXvo6pFV4n78GmRRHnT4+FM7VdSeN2hBZXA/S6l76h3BSYfwvE54nzQt9aH7SnAH1M6/nddEsUn+fx/vo0hWkG3r7HAbEKZ/PYweh1nfhO14cf12/fr34sbY5YgN2CmQ1chbdKDJ69+5dsTihUbBcoVG4fPty6tSpNuS348Smgcs9ZXH//v37cu/o2xnemvWNDXU2A7H4oatenYfrTZ/8WMOk5U2ecH2Tw1ZkH027jr58+VLy12jLknKaji47Bk+fPh1duHChhAM68uZxEKQnZavtgTKJO3PmTBuy94hWFj73bvmjBzsZvAUJf4tSXX1vBBcvXiz6AbqSVrsunz59Kld2D3mrENTPOKKNGCeMhxp2RTxPjbeDZNNYoV9w9L1kcKgfHdlx6fv2q68sdLlz506JQwa1b03X0TPlEK72Jz9hd+/e3dOXXdC2QJnkYawyntGdMcwOE+NS49fpel6mQdtrLKstkIHnRBBP/wvueQvXuOv6YJU2R98QwsHgOWT96JuTJ8Gc0zUnnkS0Qw/Mgcw92t1jvmSOoz2Y23XsuCiWctTIYt3FIgWfhI4qWGj6Fr5p0Njkx8lImxU6kK1L8k5DxmVfm60KB+07Pfzkx9CmXViAmRx8UZ73Yccwc+OU8ulzORkiNYwH9Suuaytex37U0WX0iVnKmhUefMqgXh3tYaAQhtE7y7EbRpfLIwMOP0cHlN11bHCQ5wVDTUYafYexidGJEeYvERytz4rK63qZCiFMh3nQj8143ueZu/Vtk+CledImw3GnftH0jYl68wMjbFEs3PBikfCPcjXRM6E/evSo+IE3X+2MzYMag8FEo3W9wbNL4TtZ84Ks/t0JfjW69MGIAC2SQgv+tB0KrG0WU8qt2wwdfbDLKCEcvVy2IWAhpa2lG20v2WcB/V68eFH8WrBpPxllvFn4w87Cq12jPmQMa/cMWdzo6Hrbkx7Kgx5dhgiTDbtlk/TrK4uPWV0X0nDfZwgKGVbITh5QmHTVBKp7lcn3fujL2HB9GCek4Uo8OvHc1Rz0eeE5oT4mK55lDFXGqtrt6tWrxeAT9PmkP8umPHQjj3QNIcwGcxEvX8wfvEjheN55HpkHuPc5XDvuXPW8MU9wyqD8rEtdc+mqwBzJ3MUJlebiWXVlLeLFWrrSTrInWL/1PS95mDsXSjPR76EjaA/N5FjSyHXRCDqO97NRD28U2RdGWvk5X5WfNEDdXr/K8LSE1Xm5/vzzz+MwyuirV/J6vM6PKVthkqMLzys/NANkT7jjeqk+wK9wyQ1elqf3cNJ7ud4u+P3+97///Z4414EyXfcuGainD+UlPahup+57cN3VL7pHBo/3NoA+2VwPHHjdlOP3agfCvb66TXDC2xy/ZHc8DWX5vdJ7/3h7KUx5kANcZ5xk9LKV1vF6pC/pFK7+rlF7iC5dvewuvagHp3viPB4dqJ+wEE4KjO11Zt30r/U9Vj+Sze4Sb8xdu1whhBDCcWCV19khWDf9a31PxP/jFUIIIYRwHDg2hhffqXB2zVmuzm5DCCGEEI4Tx+qoMYQQQjju5KgxR40hhBBCCGEAYniFEEIIIQxEDK8QQgghhIGI4RVCCCGEMBCdH9eHEEIIIYTF4KZW/qoxhBBCGJD8VWP+qjGEEEIIIQxADK8QQgghhIGI4RVCCCGcEH799ddytDXLL7yQ9rvvvmvvvkE+8s9axqqj9pBzjkrXuQ0vfqi6T4llwKD46aef2rthUEd1MSnupMKArB/OLugnxkcXs5axbJBhiAeMMcJYCSGEoWDOOX/+fHs3HX6KzyH/5cuXy/dIOPzHnWvXro22t7eLPpubm2Odj1LXuQ2vly9fjra2tkYbGxtF2GVCA+3u7rZ3wzFp4M4zqJcJg+bHH39s75aHBuc0MGb4Hc0+Ll26NPrw4UN7Nyx60DAKlzWevD9WwcAMIawf586dm3ld5kX56tWr7d03Xrx4UYwTgX+IdWbZnD59ulzPnj1brnCUuq70UePjx4+PxNCZtDgfhSHYxaNHj1rfcuFB3tnZae/6wbDirWLV8AeJl4ZljSfvj6MyMEMIYVbevn07unjxYnv3jTdv3uwxTvB//PixvTue3LlzZ3T79u3if/r0abEr4Ch1XbjhpeMmrhy36O0fP86PeYhTuB8nslgSVh9bsaug9H1HWuxukF9l+1aq7rmqPpWHq4+gpAOOursgj9LUdbm8pFPdng49FO9tILwMHPfkf/Lkyej+/fv7dlfUdoR7Xhkg+NV2SuthQPmStW5nz9OH8rps6FbXoXK8vFpfnGT3/lCY0qs870PS0Ea0FXGOyvE2VxhO5VAGZUvvuj2A+K7++PLly77ygDIUjvwqW/fSqa5LOhJOecqDDt4OnhanutUHejYUpnRq0xDCyYb5QQZIzZkzZ1rfyeDevXvluJE57vXr123oN45M168VHUH72Nra+rqxsdHe/cbOzk7Jj9vc3CxhugfCFL69vT0uo8+v8ggD/Lu7u8VPGuRwuFd9pMPhp5zz588Xv+ehDJWnujyfZOUqmRRX+4E6kFV1KY78+IlTPYBf8ii+hrxK0+evIdzjVB+oDuqmDIF+KpP06CDUfuB5aijb9SAP5Uln71cvnzj1A2mUn7yqryuPyvU6a7wMoAzSg8d1+aUPjrC6vx3Sk0aQzttMce6nfKXBr/rBy3IkFyCPt4nCyauy1EbeVmrrvjYNISwXnrVlMulZJlxzRT0H+PwLPpcskmXr70hfnLfLULpCre9Cd7x03NQotMeabhQtV9/Wu3HjRjn6wfL2b4OePXs23hakvGZQFL/e2rnHcn316tW+bUEs26bhyjdoHJHhuH/37t34+Of69evlyg4BYaQB6iLt+/fvyz1Ih7t375b6apRWOwbNgjb69OnTuC7uBWWj86lTp9qQb3WyS8IuRNMXJb4GGdCLXQp2VWaB7WO2VIF2o820s4N88Pz589GVK1eKH6iHtwG1IduzNch569at9q4b+l56PHz4sGzn1seQtIG3DbA7BPSHZAT6CehD8qit4fPnz+NyutquD41HUPnoTRugI30ClMlYoj2IF8ozDX+70lgljPLRgXHv8hNH2bi+NzH6jGcENP7oY5eJNtcun77Po831bGrM97VpCOHkwtzP3KP5QXMAcwjffHEEKZi3fJ04jqDjhQsXyhzLHCj74ih1PbJvvHRUgqK+KE8CAwIDRc6Nu4OgRW8WZADWsJC5TL5AzwJ5Hjx4UNqi66hHx1AsyhgAs4ChAzxILK4YQCzWtLmf6buBMw3aCoOS8uZBi7xDGAYNeuHwS2b6VIYJV4xzgf7e1vMYW9PQkRt9gTzLBMPP9RAYu3zwievTjXC9BNB/5GEixQhjchE8U15HVz/AMts0hLB6MMfqeWcu0rrKHMHGhF7wWT/wn4Q5wTdU9DnIUep6ZIYXiwULXK0onc/iBzQGCz7WuXZJ3DjR9yw1vPELGlO7XA71MOC8PIybrobnw+muHSAWOhZB7SYh7zzfyWAIoQNGBw+Ayy3YuSKub+HsgzNtFnBAJ+TEaJKBg8GrnR1gYHbpKGgrDC9kRO5ZoB+7dshoJ94u9PC7sUp70M+KExiM9KXq5qp2XwQYphgrap9lQb/ojQt8DNMO9Em9k1uDsc84Y1eMsc24xQjTGOFNzneRSUub1yy7TUMIw8MLJLDT48824brvgzmEeZC0zPl+OnBcqHVFB+3wcdXL/JHq2ixue+gI2kMz6Zc0ck4j+Di8eZMuZ8d+L39jcO1Jq3SkAd1zxTWNU8KbBXmcB8d9DWW4jMrrsjgKw3l55FO45AKFUR64HjjwutBVfm8D0pDXZeW+xvOrXGTz8C4kl3SibtcDatnqMOrxNkdWj++S13VU23sbEV/3I46ywfPLqR7vE7V/fV9T55G/1r0rXV96XI3H9+XDD97nChOSZRKSVahfHG9H/HUfiK42Vdqu/g0hHB6er3Vm3fSv9T1xP5LNDgLfks175BeGg90Xdth8d5FdGfpMV6cr7KTC+D3sEXoIYbU57uvsYVk3/Wt9V/r/8QonEz9eExyvsTWsPwoQHH3V/9dMCCGEcFw5UTte7Izou6Xt7e3BPpQL88GO1/nze/9YYXd3t5y5Y5Tpg0fY3Nw88TtAGJz668N1egsMYV3Jjtd673iduKPGEEIIYZWJ4bXehleOGkMIIYQQBiKGVwghhBDCQMTwCiGEEEIYiM5vvEIIIYQQwmJwUysf14cQQggDko/r83F9CCGEEEIYgBheIYQQQggDEcMrhBBCOCHwH1RztDXtB7GBtN99911791ted/zH5MeZWqca/tPu77//vr0bhrkNLwScpISgM6elGRIat+unahaNOvk4wYM1RNscF/iZIo3dSZPOMtqNydInwkVD2bNMyMeFeZ435i769iih7Q86P/jci959nLQ+DrPDuKh/FWQS9fzFb+jyLZLc1tbWsf/JtmvXrpVfskEffgnFdcbvv5QyFHMbXi9fviydsbGxsedjMQdl7ty5U+IZBEc92VH/MhuXSU46zjPojwoeTjco+AHq4/6zPIsygGibmzdvlrHLw1r/dqTqob/181SL5NKlS6MPHz60d4fXi372RZqyqWPZLKo/pjHP88bcddCfEaO/F2HM6Keh5oV+5Ce1GJfMvS9evGhjvqH2xjjj57fCeqIxMguM6atXr7Z337hw4ULr+wa/oTvEfLFsTp8+Xa5nz54tV8G6hz0zNEs5amSiP3PmTPEz0R/1byZSP5busnj48GHr+/abg6vOo0ePWt/JYNKu1Lx8+fJlvJgzbtwIYuF9/fp18RO37AeW5+iwLwzLMA6nscj+mMZQz9uDBw9a3+HY2dlpffPBAqhFAwOSlyXh7U3ccXj5C0fP27dv9+1mYbgJf2E7zrAJdPv27eLnRXolNhka63gPHUH7aBacr81bV3u3l+ahL2XIwfb29vieeIdyFNdMSqVs/FybSXUcJ8iPo0zSA1elawysEgZ1uMc5hHflx48ciu/K7/Ljl8xeN2HC26cL9KIctZm3V5+eajN31OnpcSBdcCpbenp7cw+SFzy+r//7+lrthEykUZinV53gOik9uPyE17pDV101SoNTvS6Lh0PdltwTjzySydukTy+nS06uhHtb45SWukDhGlveLnUanGTArzx1HXW4xyvO6cpPPR7meJuA/JKX8aIx42l9HKnNFS4ZwPN0yUt62hoow8uSDCA5KI/0+FWu0ikfTmVyVRjOURnK10VXe4KPVQ+HrvaWngpDD9Eldxge9dWymNa/GsekYbx0ofG/DJatf43Gfde8wDPkc/cyqPXdp/0sDTJNUOL0sKOop6UBdI+fssDL9HBNIECZOE1QxMkvNGHW4fg12BzSqi4gP/c48uBIM2mA1vqSR7p4nPtdXyFdXVb8lDmrnoTrYemru2538useXE+VBZIFKM/zCK9ffpzSUgZyIAN+yU4alU+82kb6caUMtQtpJKeHd9VVQ7yHezryu/5OHUc5yuv9UKcjnDCnS07C8Ne6C9fT28XT1XmUBpCpvhfUr3xccYr3seP4OHK/y1lDOR5HHZJHdXCvNgDSSwbJJlzfvjqB/KRTu6scyvT+4h6nctVvxMvv+qnPwNupz08+pa/p6w8gn9q3pm5vylFej+uTOwzPstue8jVeawjXeMTv485h3C6LIcee9NUzVbcLz8UydYVa36X/VSPfIvg5Mtt8r169Kn6Oba5fv178bJ2zTT4Jzp/5/oajskb2cvbMx4Cgj06bCXP06dOnUm8zyZQ4cL/z7NmzPdut3L9586bIQ56mU8ZHpZQ9K64L8gB6Iz9ycgTkx1iAPs3gKN9w+HYox199empr2LeFP3/+XK76poX0avMa6enwca7KU1n6vqV5SMflcfxRg06nTp0aywjohb58f0K/IRP6oSf6Kg336MkWOOVTBvUBbUC/3Lp1q9zXx4Ciq64atpv9WwZkoM6DQNt5HbTbu3fviu7IjwO1o+iSU/0/L/qug2821F5d1Mem/hE/9dP+hKvfPL3GsEMfMn4oZ9YjUdpdaTXG9L2S6uibM5CR57HrWeYbpytXrrR3+/EjuEnPdtccU8M4RAf6Vt9soUvX80a4ZAeN35pJ/XEQmsWl9f3Wzn1yh/Xi+fPn43WIcaC5qh4Lfux4nEFHnmueKeZXHTseJYP8dxJdC7RgQZ0VLTBMsAwUfdDOBEW4HBPrPNSL4jJhkEvOLsNhEn16MphkEPEXHJrkWYwIIy15Z4WBWX+8C9Th9btxKDAkkIF4NwK45zsZ5JnlGyAWRa9LC+AsfTVLXfOMu4PAwu7yq0+cWeScFcrAeJQBMQvzjr8aZKZeXqC6jKEueIZlUDDGeNHBEGbS13ehMGnO6AIZDmo8O31zTA3PnPcv+Q76vInD9scsdMkd1gvmbfU/BrrmdR8LjPtJLzLHDW1cgL/gHBVLN7zYTeItS9Y0E64maRZo/zDdFx9NvOweAAs6g4E0slyZaLFkeaPUBEk9pGES93qZmLmvJ1LerP3DWepblkXMZOx/7eX+afTpCSxeepDcGELfeRZiQfuyG0NdMhgwfCjL+6iWH5mQsV5A6HPSUgYPOm/eQoul8lIfDzz1q++QgzLqvnJZxKS6RD3ukKFvJ+IgaMwjC3Ctx90scnahNtFEgi6UzdjqMoT7UH+6XPQb4bOCwYTs8y7e9CNv3aD6MMIY4zBpzuhDY65u53khfz3H1CA/OwaC9Mja9bypbTRWfT5zFtEf0+iTO5wsMP6BnR6fgwjX/TRYUzT3HzdqXZmjtMPH1U+jeAZYa1h76mdyqTSL9R46gvbQTPAljVxNYz2P40gLzQS2L0x4es5aoWmoPem5AuV4/c1EVcI9vdJCM1mPw8jHfReeTjJw9TD5kbdG+V0X/F4usoOnUZhwPcjradG1T08Pw0lGr19lUUafbi4Peeu+Qgalxan9He8f1fnzzz/vCUcGpe0KB5fL5fD0yAg+vvrqqvE00tvL6crr+v/Jn/zJ2O+y4sDLUn84lF3LidO9dNM9dXj9yqs+ULjaXHXqvh5PUPen0D3pfQz5+ADXW2WTxnXvGiOqV+1LOejjeBmK8zDygO6p3+PxO97WzUQ79tc6kM/TSn6FSRZvF/WVh6k96n5VOV309YfLQ7k1rrfqxbluXTIqLAwP7b/OrJv+tb75kewTAFa7H6/y1szuwaofI/CGwe7icX2zCiGEg7Du6+y66V/rO8g3XmF5dB1XcjySbzdCCCGE1SM7XicA+szZ2dlZ6LchywCDkW9i4DjIG0IIiyI7Xuu94xXDK4QQQhiQGF7rbXjlqDGEEEIIYSBieIUQQgghDEQMrxBCCCGEgThSw4v/4GzS/yLLuWj+g78QQgghnBSO1PDiL9n8fzmv/2sEPkY7zv8tAv+f1qz/U3AIIYQQTj4rc9Son9w4SfjP24QQQgghHMjw4giQ3RyuON/V4X8jV7h+nwwUTlr9HhlXwjG6zrc/qEwawgnDD5SjMgmnDPzaIeOq+L4dJo40lUb1qw7yqyyFuX6ECYX5EanyeDjX3d3d8ntZlN1VVwghhBDWjK8VHUF70G+B6TfL+D0w/X4Yfv+NNNJxz2+V+W+rKczL0e+UCfx+Tx6VAeQHwvSbYyqzhryqx/3ko17ClE/1+m+bKT16ql7ClYb0+k030lCe/J6+riuEEML6se5rwLrpX+u7T/tZGoQ0MjRA9xgaHo6R4caJDBjhRhD5vO6ue88v44Yw0rlzGRxkIV7lyAhSWdAnh67uKAfDyuVy3PDqqiuEEML6wVqwzqyb/rW+C//G68uXL61vL01d5Zsnjtv8CHJW9JE9R3YcA/oPKzfGTClfrv4gX0eTV65cKWkFZZCe3zYk3o8U+/B6Xr58WcL8DwT6OEhdIYQQQjhZHNjwev/+fbliBG1sbBRj59q1a6OHDx+WcMDIuHXrVjF8+K6Jv2Lc2dkZvXnzpk0xH7dv3x49evSovfvG1atXRzdv3mzvvn0PVhs1z58/H21tbe0x1gCZkO3x48ejzc3NsU6gb8VevHhR4tDv/Pnze77Pkk67u7vj78agy7CcVFcIIYQQ1oSvFR1B+yCNH/E5Hq5jNR3HKZx7nO79OBLHd1vyc1znEFYf1+kIEaeyHK+L8pQOJ7lUj44U63BBmBzlgpePowxQGVy76lI+lRNCCOHkw7y/zqyb/rW+B/qRbNKwy3Oc/4+tPtgtawyjqW0QQgghHIRZ1tmTzLrpX+u7Mv+PVwghhBDCSWduw0v/TxW7QicR6eX/T1cIIYQQwiI40FFjCCGEEA5Gjhpz1BhCCCGEEAYghlcIIYQQwkDE8AohhBBCGIgYXiGEEEIIA9H5cX0IIYQQQlgMbmrlrxpDCCGEAclfNeavGkMIIYQQwgDE8AohhBBCGIgYXiGEEMIJ4KeffirHWrjvv/++De2GX2ch3Q8//NCG7OXHH38sbtVBB/SexCRd1V64X375pQ39DfLyG86LZG7Di850QQ8KHapGQNnD/EQPjX7UP/GDLj5IaZtFd9aQ0J5dg3DZ1O14HDjufb1oNMkdZn44CujD4yZzCILx+/bt2/ItEe7Vq1e9cynr+MOHD8ffHdWGC2Xdv3+/vVtdZnleJ+lK+6i9cJcuXWpjvkH87u5ue7c45ja8Xr58Odra2hptbGyMFanps6AFiqtT6eDLly8X/0Eg/82bN9u7/dBwpFkm1PHkyZP27tvCc5xhoC56sE0aE4qr23FVqXXhOTh37lx7d/zgeVyUkU3b3Llzp7QJv3taT+irzEn9/dmwHjAHPX78uL0blXX648eP7d1vsB5ilN24caPc37p1a/TgwYPiF48ePRptbm62d6uL5pk+JulKHHYIxlvXPLVMu2HhR40oME1gGoFBAQyWnZ2d4j8I5N/e3m7v9jOE1X7v3r09g/TDhw+t73iCcb3IRYjx0GdQseC/fv26+Ot2XEUm6XJcqSfdw0D7nDlzpvh5DjThHQeW8WYbwlFy9uzZ1vcb79+/3zO/nzp1as/YZw3HQDkJTNMVw417Nm948XcwPlmTlsFCDS86DAWwMLEimYRRBj9u2pkzeHqh+3onSeFsr3ahMmh4NSpv5Mo3aRfGj0vckFQYbtZdAvIrj7dBnyweLkfbejqFSzbF43wAcU+dhLkeYlL/fP78eRxH+YJ0Cq/blXS0C37akDo18OtySMduJwOfOG9P1eHt0qejQ52Uo/7rk7uvT3Gq0/sBf5cuhOEHb0vC1Q5d5bmujo87ya46yK+yFOZtQphQGOUJ5fFwrrQ//SAdife6aqSXnKAsnn3mAA8XlEcbcVUdXhbUbSh56rGpdNTpemlceB4v08txWbydoK9dQzguvHnzptdwqMe7w3paH7kdZ/p01SkFV+ZA36Th+V+q8dlYfHvoCNrH1tbW142NjfZuL9vb23viKK9Rap+fMjY3N4t/Z2fna7OgFb/CBOHEA3GK93DqU/4arxPZqFeQx+8F5ZEWvE7CVRZ1e9mk8bL69KYM0nFPOLjf2wI8r7eZ8nDtykMY6bnWsjoe7n7KU5m0BXFAmWobIFz33m5clV+ydlHLjn6kldyKq9MpjUP9hOOo09vL+8dlq8MpA1zmPj+oPkEZKg8ko8tCmOcRXr/7yUe9hCmf6nX9lB7dVC/hSkN6ygHSUJ78nr6uq8b7Ab38HhlUrkM6yUy82sBlVj6ukhm8PR3CPc7lVVnU42VRH/fESx7hfet5Qlg0Pu6WgZ6xLojTcwd94x5/37N3WBapv88dNZN0rUFfzY/eDqRX+EGp61z6XzU2dYy+fPlS3hynwduoW5m8aTYKl7dx8nPEQ5h2C2SV3759u1yn8ezZs9HFixfbu2/3vBXU+JkwZ+Y46uXoRFYydTedU7YyJyFZm8FRdKBszt0ph7bBsiZO1FuhQPtN4t27d+NdI7Uzu1XoStsRT12S3ZnUP7QP0BbIiC5Pnz4dXbhwoYRDM7B7dxwPSvOwj/tWbdGno6Mj0mbCGeuq3QqOM3Wez46MymUr3r+D0DFxX//U1H1F2/iYkoyEMX6pX9801jsptDM6sAvj3y1euXJlLAMygerVNx13794tY2vSM9NMQON26TsG7KrLoT2uXbvW3n07HoZalxodI9O3Xi/6gr+VEk9/USZOR5c1jG/GI6AfciMffPr0qVyfP39edBK0F2VTB7IgUw3t73lCOE7wLEzatWKOYq4QzP/MDaA5SnMHc2a923ycmKRrF8yPzCHeDuBzyyJYuuHFhOp/UdAHiwWLAQtsDXnlNFEflHqx7qNrIakX2Vmh01wHLZZ0Kg+Il0vHsyCo090ImQQLiNfBwkI+/EBZMgKdWfsHMAphmiG4LLp0nAcMMs8PGAQsxLQPxo6+N4Ou/pmGG3w8qC4jRqrXr7SC/qFOFn3SCsogPbIQP83IAa9Hz4yMyknMUtcs9R8WPtB/8eJFcX39rOcCeZg3GMe8LNCO/oIlI2xWGAPoH8Jxg2eB50BrDMfp9fPKvIPxIUOC9Nq88HlDL0qHXXOPkkm6Or42ag6UA9aAvnnoICzV8JIys3QcxgkLAzsDykejEc4bqMCvXSE1phZHT9fF1atX93xIzGTd1Ql0FB/WCQavZNE3JKA350mwOCCb50NOZKcePSCCh4QdGHV6fUavh0g7bew+sMhgoavduFI+dXKlDB6g2qidtX8oAwONNqA+Bq+g7X2XUovcrH0yK306zgpye19LLtqIxVrtjY7Q1z+zQD0+foCx57tY1FtPiOzO0E/1mEJW9EUWJkPfZVV7YKAQ1/fMaBx6m/mYFJPqAr1Bql500NhYJIxZ3ra7/irLoV/RHWg3ZGOcyyjDiPVvN9AHo24SMlDnGV8hHDWMV559ngFemnCspzybPK/c67llzte3mMQv0qgYEuYe5jV00fM6q67Mf4Th3FgdhGax2UNH0B6axaikkavRtxs4/M1AGN/Lz7mrwppJceyn7GbhGd/rjFr3HuZlkI+yu1CdzSJS7rkqH3X1oTQ46hIe3nR4CXOZSes6A+l0j1M+3Ss91zotDv3A49QPKsvbQ22BXN5fXbis8oPXpfKEl+ltM6lPFF63eV2Pp/Nw6NLRcbk8v/q+jgdPJyeddC+5VKfCu2QUhHnbgI89yeT4s6M6SYeT7KpH7VaHC8Lk9Mx4+TiNHZXBtauumlpOIZlVluNtVbeb6sNJVkCWug1rJIt0UXs5Xp/ifCwRD7pHJo/Hj4y1TiEcFMbVOrNu+tf65keyVwx2EHgr9zcQLPN65ysshq62PQ7tzThpDIQT/ayy+zboW2gIA7Hu6+y66V/ru/RvvMJ8+BGRmHbcEg4GxosfQwHb030fc4cQQgiHJTteK4Z2Mpzd3d2Ff0MTvsHulhtfGxsbx+JjUp5TYKzM8tH8cQHDV3/1mXkonFSy47XeO14xvEIIIYQBieG13oZXjhpDCCGEEAYihlcIIYQQwkDE8AohhBBCGIjOb7xCCCGEEMJicFMrH9eHEEIIA5KP6/NxfQghhBBCGIAYXiGEEEIIAxHDK4QQQjgh8J9wc7SlH4nu4vvvvy9p5PixaeE/Hn2cfiieX32R3K77JF2BfKQZkrkNr1qJg0Ln6udxaKS6MeaBwXGY/EeFt8FRQ792PWT0zWH6+bB9e1Ro8loEiyxrEn19uGxWaRzPCmNy0sK0bvi8zng9ThxHmZcF7VD/8kkNaR48eFC+OcLxyyjXrl0rccwfb968GYffvHnzWDwnzEGA3Ds7O+XXL9Bzkq7AvPXkyZP2bjjmNrz4OZWtra3y0yoo0sW0SZjO1c+00DD6iZCDQH4GRx90CGlWDW+DZTNLG9Cv/sPc4iB9g248rIft26OEn2jqG9+z4M/AYcuaBep79epVe7cYJj3HihtyHB+G+hngZ5YuXbrU3h0/9IwtAtpGY5R5/cWLF23M6nMcX+qWyaxzjY/99+/fj27dulX8b9++Hd2+fbv4KWtzc3P0/Pnzcr/KYCxKB3TzcdynK/Aj/NgzQ7Pwo0YmhGmLPAu8lKVzsVAPCvm3t7fbu/2s6qLgbbBsDtMGB+kb3jDgsH17XJnlGVg0TCBMNosC+fveBD1uyHF8GI6DcTgPesYWAT/Cf/bs2eLnBezevXvFfxw4Sb9TOhTMy86zZ8/GxgnP9qdPn4ofNC5WHcbB58+f27vfdJyk61GyUMOLBYfdJ968tf3L25S2sGc5R/X0Qvf1243CsdK7UBlsvVIu8KaufH1v9KQlTtvvLjdvmV350V3hLueksmpctr632a72VBhX2lzxoKvaQPJLLlEfvXBPuvptx/WkjBrysZ3LTldf+zgKq/vWURqcy8i92kzGjqfFSUa1v4dRlvRWnFCc/IqXk25qJxwy4OpnwMsCbwsPV59wJa5vrPTlF4rz/lE74SS76uHqbYdfxxXcU5+YFKc6XG5vO9VbQ3rKUR/1yU1Zwtsdpzq79MQPPg8Qhi5qA91LXuX1eNfVkdw4l5174gijbJzKUXrChOvkKEzhpKufMeK8rhrVLac05MGIxjD1cEF51KF2IL2Xhd/1UR/hr8empwP5pQPplcfTejmEIwNhKkf4WPOxEibjxsnVq1dHT58+be+ODxwfYlRNozbEjoyvFR1B+2jecL82b9ft3V62t7f3xFFeM0ns81PG5uZm8e/s7HxtJsXiV5ggnHggTvEeTn3KX+N1Ihv1CvL4PZCGPDiVj59wysEvyK9w1xkZuZ9UFngb1O3h9QjClQaoQ/dcpUudH7/aQHKSVm1GmMso2YFyVRbx3s6exyGNwiWL5PQ49xOvNA6ySHaVxT158Xv/udx1e0pu+dWXOLWJ5FGc58GBtxt1KW+fvy6Le8UBMnJPHslDHcrXheSs/ZSjPIThpxzK9nYij+6pX37lgUn113Hk5556PK5OR72kcaifNDjq97ajXO9DlVWHe9v21Y2fMCC939ftQ5ngsqg85RFev/spj3uc8nHFSXauKl9jAMircMqTPIRJTg/vqqtGcgH1kE4gh+svlA5HvHRQWS4PdUtm6CoPKFP6A2VJXunPvZeldpJ+ONdR95LxOIDMy4TyNT4mQZur3QV9onbG1fGLYBn6u8y4Wv8uXYFx4+NtGdT6Lv2vGps6Rl++fNn3dtIFbzJ+/srbVPMglTc78vNmRpjeZrRlqDPpaWARX7x4sb37ds/ZsKOjk+ZhH5fPPbtqnA+D3qqQjW1ZzpJ5UxAc+7DjMamsGuRAP8rVd1H12ye7T1euXGnvvtXz+vXr9m42kJM6aAdt03O80DxsxU+dkh28P969e1d0lv7g27t9UDayCvL09a3DPTLqLYU2pC3pB8l+/fr1cgXS+Ta5yiMf6XlrV9uStnkwi2zoL5BNcYL8ON622RlQ3bQReXlj7/vOsC5rlrHiRz11mwD1nzp1qrQbbeg0E0u5Im8zmZS2mmXcHxbk1pgB5O57XhwdkSI3MoPat++ZOHPmzJ52URvQ1sw39JPGcxcqX1y4cGFPe2hM85xq51LlMZc5XWMLaG/ueWaQCdkkp56Fu3fvlvKBK2OIujTGfPxC3zFgV10O86XvGtFPtPm0XaG+Mak5x+uRjNPKpK3pU5B++hZHY6PvGaEOxgmy1zo+evSoXOvwMBnGuD+3wNhjHGl+rONXFWTGaYxoTIouXY+KpRtePPAPHz4sDTIJJiU6nMmjRg2K80XyIMxiKEyCDnV5NCHxncRhYcB42V2TSL1wzQvyUjZ9wiQ/L0zCLuNhB7KX1dW3tWExCSZoHZlwVXlM8NLVjaB5oAwWRs/PIkO5GMMyeGbhsGOFFxS21mmzSQYGnD59ulwPO+4PSt/zMitdz4TGHG2PweF9QhgT7DzjRs8ZfYzR5mMaw8PrryfzvrFFOtID8dMMEkBm1SPjcBY9ZqlL5S0TXoB5OaQN/YXIoa1l9GFg8RLA0RbtiEEt5n1GMAYX/TKxDtDuXdCHPFtDjJtFwgsQ3z92rSV9uh4FSzW8NAHMYiyxgNDJPDzKx0NKuL4BAPy87TMhMThAE62n64K3KP8oFSOvb7fMd5KYGNj54W2NNy/VS0fS0bxx+o4N8mOgiK6yapDNd00otx4oLPAYFIIdhTt37rR3v01WMl5ZoGv0jQZ9ognQ0SJE/eBlSU/l4aq2mJe+vnWURrIAbdln7JHfFy/BBF+/tc8LcmAEaOFFJhYZFuZ5jM9pY2Ua5GMMTpsQKZc0yDtt3M8ybg5C3/MyK33PBOUhv/pZfUI4Y1o7SvNAedo1EXre1FeUXz8vfWMLWUlPOGNEbQsqQ3kBuetnoWv8d7XfpLqA9vH5EjQ2FgnPAc8nxpTmkS7oV307KhloC8YLHOQZoV7K7WqfdUMvAhhOGmtca6Oc8VCvf4w70tGHPoeuOsxZyM1c1jU3dukKjBeeceYpn/fwL2oe7KRp3D10BO2hmSBKGrma5q1vHIe/mTjG9/I3C9g4rFF67KfsZuIY35MfdO9hXgb5KLsL1dk8uOWeq/JRVxeEu56ezvXDiVoe0VeWp1eYyyZ5a0jblcblUn3C2wCne8np8jUT9MSyXO6+Nlce1YMjzOuZ1Lc1ngb5wMsW3n5ylEke3SvfJNl+/vnn3jg5+sHbSeUhg4fXZUHXWKnHg/y4GpdH9apdFI5zvG003sBlVbmiK71QnLdjl9xevsKcWi751S4ejx+8reQko+69n8H7x2V2uKdsp0smp2tskY583k+eVuGSTbhcksPLx9GeoDL66qqpyxGer5Zn0pj0fvE2Ix1uEpJFukh+x+tWnI8ljQVvM4+nTPX1KoKM68y66V/rmx/J7gArGMv5IG/ONYssK0yGtu7aeTjMTldYPXh7ZXfEd1WOQz+zg9MYAid6fqUfOGactOMVss6um/61vkv/xiuEIWALvf4zaBZoji3CyaL+QwYdSYWjh5fMGF0hTKGxwvbQEbRW1Nvrh2GRZYXp+PEHTscR4WThzxVuVY+Tao6bvLPix5j4w3Roq3Vm3fSv9c1RYwghhDAgOWrMUWMIIYQQQhiAGF4hhBBCCAMRwyuEEEIIYSA6v/EKIYQQQgiLwU2tfFwfQgghDEg+rs/H9SGEEEIIYQBieIUQQgghDEQMrxBCCOGEwC85cLTlP4hdox+Vlvvuu+9KuPK642egjjN9ugqPm9Rmi+RAhldX58jVSh0E/ZI6btLPgdRyMECW+oviKwS6ovMi2luoPfmF+lXC+5mfAepj1nHj6KFclM6r2oYhhJMP8w+/BzoJ0jx48KB8c4Tb3d0dXbt2rcS9f/9+HI7b2to61j+7NklXYB11fS9dutTGLJcDGV50zvb2dnFA5yD0zs7O6M6dOyVsEtMsaJTf3NwsA6jvd79Y2IgnnRoNPnz4UK7LYlWsf34QGP19EB0EBuYkY2YVYAxorPHjyH3MMm5q9OPlV65cKdcQQjiuMO9pLZyEGxis57du3Sr+en7ltzeHMkaWRZ+urH3379+f+kK/FJpO2kNH0D70e1z6bbzG4Cr3s0Ae3DSaxbM3nertip+l7IPC7y1ubGy0d0eLfh/tsL8BOW//HRXq82lMGjdd6Hf/jkMbhBBOBrPMZYdhnjmtb01jjVnWerps/ftwXWXHLGotnUSt7z7t52kQlOhLjxLE4dR5usepAbSgcu+NQhiLYo0aCTcNpcORz/NqUOJXg0sf4iS/ZGBBV178wsOB9JSjuhTP1dtEoD/hMgDIB13tB0onh6zT9AJPgyx1OXLqA6XnKr8/yLXejucFykQW8ns5usdRnuhqE8qQbKpb90L1akzhKMOp5VY7g/JJbpAM1KV0QD7Vj59ylaaWizA5lU25pJPMtZwhhJMLz/wyoXyfryfh64vDnDRrGfOybP376NNV8/CyqMveV9M8lZPWF0zhC5gWNvxaxLT4eBzlqFHqBdpRnr4GBDWi0rhf+UH1aNGT3FoUPa6WSXVw73pw5V4LPPGUxT3hXqfaQ1ccSA7K87I9r+sBfu/p6nvVBcglI8FlV3ovA/kJ79LbUXydRu2vciQHV9Xn8umKA64qA5mJr1E50qlLv1pu4tU36jPSgstF3cSD+kdlkI44b0PQveSW39OpTsoIIawHPPPLZNY5RXNuF5o7l8Gy9e9ikq6guXkZ1Poe+K8aOR+F+hsjPnB+8uRJ+e6L82bOiAH/27dvx37gfLVRttw3Co+/s3n37l25TjpbPnv2bOvbjz5s5vsdyan0b968GX98qHp0rk1a4pDz9u3bJUxxSnvq1KlyRW9kB/SQvuRH52fPntHSRYdXr16Vb9/4LkucPn16XDb1kRY3qf0ePnxYyr9x40YJbx6MEgeT9EIXZCUfjnoAudQXnH0DH1Iic/PQlvtPnz6VK/oS3qW3Q3nN4C7+z58/j+UXtAPl3Lx5s8iPPKSDSW0CjA/+mAB9yFcjvV++fFmu/r1fn9x1GlCbeHszNhTPuFJZhDMm0MvbEPrGIeV3jZMQQhgS5tmuuRQ0D54UJukqBtO5mfT30BHUCZYjaWsLslmQ9pSBX5Zzs9iM/cqPVY6THyijz9puFsySlmsX9a6D0qts/JQvP07gR0ao9UAexUleOZVd73BAl27cQ1cb1vXip+5aL/xej9/jx0Fdv1C46lY7CdeFNKRXHrm6TKF0aq+6HOmt/OjHPShOcoHC5JBVZTnUoXEjGUgrv5zy1f3l47OrveUHtRdlKb/CoM7v6et6QwjrheaJZaG5Zhqa72qYX30OXjTL1r+LPl3B5/FlUOu7T/tZGwQhScsi4hBeGy+kqRciLaaEk97r5V7puiCtpwfSU5/qoXz51aAuA52Aoy7if/7553E+8EUYvBw6iXsNbMrTQCWceoSHKZ/qoDy1lfAw/F15Fa5y5tFLsnJV2aA8DvE4yoE+vWuUTtTlkId76pZf5ZCmq01UHnH46zRAGDLJrzR9cqtuwr0NQfekUf3KD8rr4d6Gnl/+Lv1DCOsHz/+yoGw5zU1c/R6Yh3Bd+Nq3DJap/6y6ag3EDa3vPu2nNYgWEXe+gEhpnC+Odbjfa2HDaVGS00JY42nqdDSiwr2xvU78yKE0qpdwUByu1rmOV919RgNhXpcgTIuxqNvJUbi3F7pO0gu8PVxWD1MenNqgazB6ur6+IX+tZ43KwPn4IW3dJsghWaizziO8zFr2LrlVFngfK6/uva0kGzrW+tdtSDm69/agjK42CSGsB8wJ68y66V/rmx/JXjJ8k1R/3xVCCGF9Wfd1dt30r/Wd2/AiPoSwWNZpEgph3YnhFcNrj/brPiAWBX+Jd/ny5fZuVP5q86T9lUgIIYT5ieEVwyuGVwghhDAQMbzW2/A68P/jFUIIIYQQ5iOGVwghhBDCQMTwCiGEEEIYiBheIYQQQggD0flxfQghhBBCWAxuauWvGkMIIYQByV815q8aQwghhBD+//bOHaeRLQjDXgciQGIPBAgREGCWAERESLAASAhJjIhBIiLCBCyAISDAEgFrQAghxDbu7e/Qv29N0+2xe+ie6/H/SUen+7xfuMpVZ8amBax4GWOMMca0hBUvY4wx5i/g5eUlubUI+/v7eepXYjmCKKYTTk5O8tzp5vr6Os2H308usrGx0epcJ1a82My4KcXwXWiRCByGSHEM/DxPfCdQJx6iYhvjwAZRd9QBBjZL/cRQtsGToMNAqDN+Y4wxswOyirtEhPPz8yQby1hfX+/0+/1Ubm9vbyjjnp6ehvUJvV6vs7y8nPKmGWTp1tZWmtPz83Oe+p+iyc/5kXdwcJDnNEstixe/O8ggu91u6fuv4DBUHQixublZ2d7Z2dkwj4OxsrKS+l9cXExpHCgWkjAYDGr/TuLl5WX+NBo2i36AQ8xYGBf9suF1QJn78eNHmgt8fHyk2BhjjCkD2SiQkcjGKubn51O8sLCQYlhaWsqfPnl9fR3ZxjQgKxZyuYgU0LhubTCx4sUAq5SY29vb/Gk08cej63J0dJTii4uLFMPx8XGKo8L0+PhYS+n6XfQtIWrXk8CBB/4QODDTfviNMcY0i2QdHiPJyDJ2d3c7Ozs76RkZKsUjysq/xctyeHiY5lX0HrFGgL5Ael0jSR0au+MV3YEyY8qsByhf0kTl0ptk8igiWLiwKsl69vb2lmIsRVWHRv0QVC+6CkkjLroXlV6WVwYKH0R3o+orTetB4BBoHRgPZmJgjpqLyhI09uiS1RhZQ7XFWGO7mmscF6iM6kOcc1yjuLaUjWVEbM8YY0w78JmMWw0ZWyUH8dJg7eHz+e7uLk/9GdyO29vb+dt0ggxDhq6uriYDBh4p5g0PDw/pGQWVPPSGKMMaJevwJ0qSKul2u6l8pvzkKZ/0+/2UPhgMUuCZNJ6ziQ/zoNfrDd+Vp/aq2heqSz2gD9XhmRDrkpdtQnpWX4J03ilDHkFj1zPtVaGyMdCW4FnvxIwdNAcC7RODxhfXomrs5PFOPu3xHsdDG5of+eqTMhDfi/3qPbZJeVA9yhbHF58JxhhjPuFzs2noo+qzl89yyUjKSRZE+ExvijbmD8wvzgNZpb5JjzIdedaUrCrOtxGLl1x9c3NzKQBpZe4yNO9sHJ2rq6uhlWfc+0xy56Gxo9nis5b5lP6wgMl0iuaPRitLj/zaMjcK3KWYXaPPl7p8e5BFbRTZxqX5EOR6Vd8EvmEQ39/fpzyRHYB0r416RcYdO+vAekaXL+OJ5uN4efD9/T3F4+xBmdlabl7ap0+5VWkjO+BprjxXfaMyxhjTDMiUKpBnXGNB5lBOcjMS5ca0gk5Qdd2H+Y0j05ugMVfjuKBUIKAxBaIkTEJ0N3K/i3ddDkRR+S7YOMaGr5jx1iXTsL8oZf8HJt0D3T8bBfuiuda952aMMaYeGB9GuQpxJQp9qRd8qUceTDsy9shIcXNzM5RxrA0yXWBIaMu12ojiJe0Zq4ksJ2UaNZyenqYYhamOZYRLgrC2tpZitFgUHIj/DFbpUgKkPKDx/wr8wFjAqI9CMSn0TT2UQSlu495lg98Z+zjU3QP5yuUX1903xooyrHtok8zVGGPM5MQ7uQS8FFI8lKfP5MFgkO6BkUZcNASgtH2XfGmb4lyRaZorniZ5s1ibXq+X0gky3rTCPwVKkkqhXAzRVwqZVjnM41nonpDq6J6QyhFzP4g8pWeCPK/9FflsiQVt0kYZapMgv3ZxDII2Ylk9E4r9xbyyvjVOhao0KLanvmKaxl61TnHscS9iOgGq9iDOmXTa17v6j2nxDCiNENfKGGNmHT4XZ5lZm39xvv6RbGOMMaZFZl3Oztr8i/P943e8jDHGGGNmBStexhhjjDEtYcXLGGOMMaYlrHgZY4wxxrSEFS9jjDHGmJaw4mWMMcYY0xKl/52EMcYYY4z5HqKq9UXxMsYYY4wxzWBXozHGGGNMS1jxMsYYY4xpCStexhhjjDGt0On8C+qAjz+cH7kxAAAAAElFTkSuQmCC\" width=\"606\" height=\"645\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe findings showed a highly positive significant correlation between the transformational, laissez-faire leadership styles and subjective measures of leader effectiveness with patient care decision autonomy (p\u0026thinsp;\u0026le;\u0026thinsp;0.001). However, transactional leadership style is not correlated with clinical decision-making (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, it shows a highly positive significant correlation between transactional leadership styles with work performance (p\u0026thinsp;\u0026le;\u0026thinsp;0.001) while an insignificant correlation with transformational leadership style, laissez-faire leadership, and subjective measures of leader effectiveness (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). (Table 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;(4): Correlation between Transformational, Transactional, and Laissez-faire Leadership Style, Subjective Measures of Leader Effectiveness and Patient Care Decisions Autonomy and Work Performance (N\u0026thinsp;=\u0026thinsp;102)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eLeadership Style\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePatient Care Decisions Autonomy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eWork Performance\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerson correlation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerson correlation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransformational leadership style\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.453\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransactional leadership style\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.472**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaissez-Faire Leadership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjective Measures of Leader Effectiveness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.407\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e**Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003cp\u003e* Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of a stepwise linear regression model that inspected the relationship between nurses\u0026apos; autonomy regarding patient care decisions autonomy as the dependent variable and personal characteristics and leadership style as the independent variables of the nurses studied. The model selected transformational leadership style effectiveness as the only significant predictor of nurses\u0026apos; autonomy regarding patient care decisions autonomy p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, indicating that the transformational leadership style effectiveness had a positive and significant effect on nurses\u0026apos; autonomy regarding patient care decisions autonomy. The table also shows that other variables, such as age, gender, educational level, years of experience, and different leadership styles, were excluded from the model because they did not significantly affect nurses\u0026apos; autonomy regarding patient care decisions autonomy.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStepwise linear regression model with autonomy in nurses\u0026apos; decision-making as the dependent variable, personal characteristics, and leadership style as the independent variable\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAutonomy in nurses\u0026apos; decision-making as the dependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransformational\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eR (0.453), R\u003csup\u003e2\u003c/sup\u003e (0.205), F value (25.841), P value (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eExcluded variables Age, Gender, Educational level, Years of Experience, Subjective Measures of Leader Effectiveness, Transactional leadership style, Passive Avoidant leadership style.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding work performance Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e reveals that the model selected transactional leadership style as the only significant predictor of nurses\u0026apos; work performance p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, indicating that the transactional leadership style positively and significantly affected nurses\u0026apos; work performance. The table also shows that other variables, such as age, gender, educational level, years of experience, and different leadership styles, were excluded from the model because they did not significantly affect nurses\u0026apos; autonomy regarding patient care decisions autonomy.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStepwise linear regression model with Work performance as the dependent variable, personal characteristics, and leadership style as the independent variable\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eWork performance as the dependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBeta Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransactional leadership style\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eR (0.472), R\u003csup\u003e2\u003c/sup\u003e (.223), F value (10.354), P value (0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eExcluded variables Age, Gender, Educational level, Years of Experience, Subjective Measures of Leader Effectiveness, Transformational leadership style, Passive Avoidant leadership style.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn healthcare settings, effective clinical leadership plays a crucial role in maintaining a high-quality healthcare system that consistently delivers safe and efficient care [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study investigated the effect of the role of leadership styles in fostering work performance and autonomy in nurses' decision-making. The study revealed that 57.8% of nurses are aged between 26 and 35, with an average age of 31.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34. Most are female, with 56.9% having a bachelor's degree. 43.1% have less than or equal to 5 years of practical experience, with an average of 7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11 years. Additionally, our findings showed that nearly half of the nursing managers often follow transformational leadership, followed by transactional leadership. In contrast, less than a quarter of the nurses\u0026rsquo; managers follow laissez-faire leadership. Regarding the leader effectiveness of nursing managers, it shows that less than half of them lead effectively.\u003c/p\u003e \u003cp\u003eA previous study supported these findings by demonstrating that head nurses obtained the highest scores in transformational leadership, followed by transactional leadership and passive-avoidant leadership [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, Mohamed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] conducted a study on critical care nurses. He found that staff nurses perceived their leaders to have a democratic leadership style as the highest, followed by authoritarian leadership, and finally, laissez-faire leadership. In addition, a study emphasized that transformational leaders invest their time in educating and coaching nurses, focusing on identifying and nurturing their talents, providing professional and personal development guidance, treating subordinates as individuals, and attentively listening to their concerns and uncertainties [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This approach enhances nurses' efficiency and dedication toward achieving established goals.\u003c/p\u003e \u003cp\u003eThe study found a strong correlation between transformational and laissez-faire leadership styles and patient care decision autonomy, while transactional leadership styles showed a positive correlation with work performance. However, there was no significant correlation with clinical decision-making or leader effectiveness. These findings are further supported by Thabet et al. who discovered a significant correlation between supportive leadership styles and nurses' autonomy regarding patient care decisions autonomy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They found a positive correlation between directive style (p\u0026thinsp;=\u0026thinsp;0.006) and analytical style (p\u0026thinsp;=\u0026thinsp;0.007), while team administration style exhibited a negative correlation with conceptual style (p\u0026thinsp;=\u0026thinsp;0.001) and behavioral style (p\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e \u003cp\u003eSimilarly, a recent study by Ariani et al., confirmed that leadership style significantly positively impacted job satisfaction and nurse performance in Dumi Public Health [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Transformational, transactional, and democratic leadership styles all positively and significantly influenced nurses' performance, while transformational, transactional, laissez-faire and democratic leadership styles positively and significantly impacted job satisfaction.\u003c/p\u003e \u003cp\u003eFurthermore, the current study aligns with the findings of Zarina et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], who reported a positive correlation between leadership style and quality of work life (QWL) among nurses. Their results indicated moderate correlations between the variables, with transactional leadership style showing the highest correlation (r\u0026thinsp;=\u0026thinsp;0.688, p\u0026thinsp;=\u0026thinsp;0.000), followed by transformational leadership style (r\u0026thinsp;=\u0026thinsp;0.617, p\u0026thinsp;=\u0026thinsp;0.000), democratic leadership style (r\u0026thinsp;=\u0026thinsp;0.600, p\u0026thinsp;=\u0026thinsp;0.000), and autocratic leadership style (r\u0026thinsp;=\u0026thinsp;0.487, p\u0026thinsp;=\u0026thinsp;0.000). It was further suggested that implementing a transactional leadership style could enhance the quality of life for nurses in their workplace.\u003c/p\u003e \u003cp\u003eIn contrast, Mohamed reached different conclusions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They found that the dominant leadership styles among head nurses were democratic, authoritarian, and laissez-faire. Their findings also indicated high agreement among staff nurses regarding their perception of clinical decision-making autonomy. Furthermore, their study revealed no statistically significant relationship between nurses' clinical decision-making and overall leadership styles. Similarly, no statistically significant relationship was observed between nurses' clinical decision-making autonomy and leadership styles.\u003c/p\u003e \u003cp\u003eThis finding demonstrated that transformational leadership style was the only significant predictor of nurses' autonomy, with other variables like age, gender, education level, years of experience, and different leadership styles excluded as they did not significantly affect nurses' autonomy. Contrary to the previous statement, a study found significant correlations between the leadership orientations of nurses and various factors such as age, sex, employing institution, years of experience, managerial position, and satisfaction with the current unit of employment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. They also discovered a significant correlation between nurses' clinical decision-making skills and age, sex, and occupational status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, they identified a significant correlation between the mean scores of the Leadership Orientation Scale and the Clinical Decision-Making in Nursing Scale (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe study found that transactional leadership style was the only significant predictor of nurses' work performance, with other variables like age, gender, education, years of experience, and leadership styles excluded as they did not significantly impact nurses' autonomy regarding patient care decisions.\u003c/p\u003e \u003cp\u003eA recent study conducted by AlFlayyeh \u0026amp; Alghamdi demonstrated that the effects of four alternative leadership styles, transformational, transactional, authoritative, and laissez-faire, on worker performance were examined using linear regression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Their results showed a significant positive relationship (β\u0026thinsp;=\u0026thinsp;.262) (p\u0026thinsp;\u0026lt;\u0026thinsp;.01) between transformational leadership and employee performance, β\u0026thinsp;=\u0026thinsp;.176 (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) between transactional leadership and employee performance, and β\u0026thinsp;=\u0026thinsp;.305 (p\u0026thinsp;\u0026lt;\u0026thinsp;.01) between authoritative leadership and employee performance. Nonetheless, no significant correlation was found between the laissez-faire leadership style and the performance of employees (β\u0026thinsp;=\u0026thinsp;.041) (p\u0026thinsp;=\u0026thinsp;378). The study's overall conclusions corroborate the notion that transformational, transactional, and authoritative leadership positively impacts employees' performance in healthcare sector.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResearch Limitation\u003c/h2\u003e \u003cp\u003eThe research limitation was that most of the responses were from one region, which caused a generalizability limitation. The methodology of the research uses a cross-sectional design and a self-registered survey.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study concluded that a transformational leadership style significantly impacts nurses' autonomy regarding patient care decisions autonomy, whereas a transactional leadership style positively impacts work performance. Therefore, hospitals and nurse administration need to improve the autonomy in nurses\u0026rsquo; decision-making to improve work performance and patients' health care.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplication for clinical practice\u003c/h2\u003e \u003cp\u003eThe study's conclusions point to the need for additional research that includes samples from a variety of nationalities if we are to fully comprehend the varied lifestyles of nurse managers in hospitals. It also highlights the need to address the cultural implications of gendered roles and responsibilities and give nurses equal opportunity to participate in shared decision-making and access high-quality healthcare. Healthcare practitioners should get education regarding tailored approaches that address distinct decision-making difficulties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications for nursing administration\u003c/h2\u003e \u003cp\u003eThe results of the current study have several consequences for nurse management, hospital administrators, training, and research. When choosing a leadership style, administrators must consider nursing and patient happiness. This style must also align with the institution's beliefs and objectives.\u003c/p\u003e \u003cp\u003eNurse managers should also receive training on the different leadership philosophies and how to apply the style that best advances the organization\u0026rsquo;s objectives. To enhance the educational opportunities for nursing students, the nursing curriculum should integrate clinical leadership practice and a variety of leadership philosophies. In the clinical setting, nurses should become familiar with various leadership philosophies, as well as the benefits and drawbacks of each, and how each is likely to affect the satisfaction of both patients and nurses. More research is required to determine how culture affects nurse managers' use of particular leadership philosophies.\u003c/p\u003e \u003cp\u003eNursing managers should often ask for feedback on their leadership style and make any required modifications. Hospital administrators should offer additional assistance to nursing managers through training in successful leadership, enculturation, and team building to create a common vision for implementing optimal leadership.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eAn in-depth qualitative investigation that provides a comprehensive view of nurses' performance may be helpful to ongoing quantitative studies on nurse performance. It could be interesting and useful to conduct studies that examine how nurses view their role in performance. Transformational leadership training is the best for supervisors because it is more successful than other forms. Head nurses should take classes on transformational leadership and performance evaluation. Programs that instruct head nurses in leadership are necessary. Head nurses must seek feedback on their management style and follow up frequently to see how the nurses are performing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eThe study approved from the Local committee of bioethics (HAP-09-A-043) at Northern Border University\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eInformed consent to participate in the study was collected from each participant online before they completed the demographic data. For participants protection, the study did not expose the faculty to any diagnosis or treatment. This means that the research activities did not pose any direct or indirect harm to the participants' health or well-being. The study adhered to the principles outlined in the Helsinki Declaration, which is a set of ethical principles for medical research involving human subjects. This declaration provides guidelines for protecting the rights, safety, and well-being of participants [26].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest has been declared by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation and acknowledgment to the Deanship of Scientific Research at Northern Border University, Arar, Saudi Arabia for support this research work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKingdom of Saudi Arabia Vision 2030. (2016). Retrieved from: https://www.vision2030.gov.sa/en/programs/NTP\u003c/li\u003e\n\u003cli\u003eKrizanova, A., \u0026amp; Michulek, J. (2022). Does Corporate Culture and Leadership Influence Employee Work Performance? Evidence from the Slovak Republic. \u003cem\u003eEconomic \u0026amp; Managerial Spectrum / Ekonomicko-Manaž\u0026eacute;rske Spektrum\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 37\u0026ndash;48. https://doi-org.sdl.idm.oclc.org/10.26552/ems.2022.2.37-48\u003c/li\u003e\n\u003cli\u003eZainal, N. H., Musa, K. I., Rasudin, N. S., \u0026amp; Mamat, Z. (2023). Multilevel Modeling of Individual and Group Level Influences on Critical Thinking and Clinical Decision-Making Skills among Registered Nurses: A Study Protocol. \u003cem\u003eHealthcare (Switzerland)\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8). https://doi-org.sdl.idm.oclc.org/10.3390/healthcare11081169\u003c/li\u003e\n\u003cli\u003eCummings, G. G., Tate, K., Lee, S., Wong, C. A., Paananen, T., Micaroni, S. P., \u0026amp; Chatterjee, G. E. (2018). Leadership styles and outcome patterns for the nursing workforce and work environment: A systematic review. \u003cem\u003eInternational Journal of Nursing Studies, 85\u003c/em\u003e, 19-60. https://doi.org/10.1016/j.ijnurstu.2018.04.016 \u003c/li\u003e\n\u003cli\u003eAlsadaan, N., Salameh, B., Reshia, F. A. A. E., Alruwaili, R. F., Alruwaili, M., Awad Ali, S. A., ... \u0026amp; Jones, L. K. (2023). Impact of Nurse Leaders Behaviors on Nursing Staff Performance: A Systematic Review of Literature. \u003cem\u003eINQUIRY: The Journal of Health Care Organization, Provision, and Financing\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e, 00469580231178528.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eVirgina S. Palweni, Jacobeth M. Malasela, \u0026amp; Moreoagae B. Randa. (2023). Nurse managers\u0026rsquo; leadership styles as an impetus to patient safety in an academic hospital. \u003cem\u003eHealth SA Gesondheid: Journal of Interdisciplinary Health Sciences\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(0), e1\u0026ndash;e7. https://doi-org.sdl.idm.oclc.org/10.4102/hsag.v28i0.2344\u003c/li\u003e\n\u003cli\u003eThabet, M., Eman, E., Abood, S. A., \u0026amp; Morsy, S. R. (2017). The effect of problem-based learning on nursing students\u0026apos; decision-making skills and styles. \u003cem\u003eJournal of Nursing Education and Practice, 7(\u003c/em\u003e6), 108.\u003c/li\u003e\n\u003cli\u003eAlshahrani, F. M., \u0026amp; Baig, L. A. (2016). Effect of Leadership Styles on Job Satisfaction Among Critical Care Nurses in Aseer, Saudi Arabia. \u003cem\u003eJournal of the College of Physicians and Surgeons--Pakistan : JCPSP, 26\u003c/em\u003e(5), 366\u0026ndash;370. https://doi.org/2316\u003c/li\u003e\n\u003cli\u003eMorsiani, G., Bagnasco, A., \u0026amp; Sasso, L. (2017). How staff nurses perceive the impact of nurse managers\u0026rsquo; leadership style in terms of job satisfaction: a mixed method study. Journal of nursing management, 25(2), 119-128.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e doi: 10.1111/jonm.12448. Epub 2016 Dec 5.\u003c/li\u003e\n\u003cli\u003eBarr, J., \u0026amp; Dowding, L. (2022). Leadership in Health Care. Sage\u003c/li\u003e\n\u003cli\u003eMajor, D. (2019). Developing effective nurse leadership skills. \u003cem\u003eNursing Standard (Royal College of Nursing (Great Britain) : 1987)\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(6), 61\u0026ndash;66. https://doi.org/10.7748/ns.2019.e11247 \u003c/li\u003e\n\u003cli\u003eAsiri, S. A., Rohrer, W. W., Al-Surimi, K., Da\u0026apos;ar, O. O., \u0026amp; Ahmed, A. (2016). The association of leadership styles and empowerment with nurses\u0026apos; organizational commitment in an acute health care setting: A cross-sectional study. \u003cem\u003eBMC Nursing, 15\u003c/em\u003e, 38. https://doi.org/10.1186/s12912-016-0161-7\u003c/li\u003e\n\u003cli\u003eAbualrub, R. F., \u0026amp; Alghamdi, M. G. (2012). The impact of leadership styles on nurses\u0026rsquo; satisfaction and intention to stay among Saudi nurses. \u003cem\u003eJournal of Nursing Management, 20\u003c/em\u003e(5), 668-678. https://doi.org/10.1111/j.1365-2834.2011.01320.x \u003c/li\u003e\n\u003cli\u003eSamuel, H., Sehar, S., Afzal, M., \u0026amp; Gilani, S. A. (2018). Influence of supportive leadership on nursing clinical decision making in critical care units at tertiary care hospital Lahore. \u003cem\u003eInternational Journal of Nursing\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 45-71. https://doi.org/DOI: 10.15640/ijn.v5n2a5 \u003c/li\u003e\n\u003cli\u003eMohamed, N. T. (2018). Relationship between leadership styles and clinical decision-making autonomy among critical care nurses. \u003cem\u003eEgyptian Nursing Journal\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 102.\u003c/li\u003e\n\u003cli\u003eAlFlayyeh, S., \u0026amp; Alghamdi, A. B. M. (2023). Leadership Styles and its Impact on Employee Performance: An empirical investigation of Riyadh Private Hospitals. \u003cem\u003eJournal of Population Therapeutics and Clinical Pharmacology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(15), 19-33.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eRowold, J. (2005). Multifactor leadership questionnaire. \u003cem\u003ePsychometric properties of the German translation by Jens Rowold. Redwood City: Mind Garden\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eArkadiusz Mirosław Jasiński, Romuald Derbis, \u0026amp; Linda Koopmans. (2023). Polish adaptation and validation of the Individual Work Performance Questionnaire (IWPQ). \u003cem\u003eMedycyna Pracy\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(5), 389\u0026ndash;398. https://doi-org.sdl.idm.oclc.org/10.13075/mp.5893.01419\u003c/li\u003e\n\u003cli\u003eBlegen, M. A., Goode, C. J., Johnson, M., Maas, M., Chen, L., \u0026amp; Moorhead, S. (1993). Preferences for decision-making. Journal of Nursing Scholarship, 25, 339-344.\u003c/li\u003e\n\u003cli\u003eXu, J. H. (2017). Leadership theory in clinical practice. \u003cem\u003eChinese Nursing Research, 4\u003c/em\u003e(4), 155-157.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAbdelhafiz, I. M., Mah\u0026apos;d Alloubani, A., Klaledeh, M., Mutari, N., \u0026amp; Almukhtar, M. M. (2015). Impact of leadership styles among head nurses on level of job satisfaction among staff nurses. \u003cem\u003eEuropean Scientific Journal\u003c/em\u003e.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e 203-216 chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://core.ac.uk/download/pdf/328025196.pdf\u003c/li\u003e\n\u003cli\u003eStyron Jr, R. A., \u0026amp; Styron, J. L. (Eds.). (2017). Comprehensive problem-solving and skill development for next-generation leaders. IGI Global.\u003cspan dir=\"RTL\"\u003e\u0026rlm; \u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eAriani, N., Sansuwito, T. B., Prasath, R., Novera, M., Sarli, D., \u0026amp; Poddar, S. (2022). The effect of leadership styles on nurse performances and job satisfaction among nurses in Dumai public hospital: technological innovation as mediator. \u003cem\u003eMalaysian Journal of Medicine \u0026amp; Health Sciences, 18\u003c/em\u003e.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003e 229-\u003c/li\u003e\n\u003cli\u003eZarina Begum Ebrahim, Siti Aqilah Hafidzuddin, Muna Kameelah Sauid, Nurul Ain Mustakim, \u0026amp; Noorzalyla Mokhtar. (2022). Leadership Style and Quality of Work Life among Nurses in Malaysia during the COVID-19 Pandemic Crisis. \u003cem\u003eProceedings\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e(99), 99. https://doi.org/10.3390/proceedings2022082099\u003c/li\u003e\n\u003cli\u003eG\u0026uuml;rsoy, E., Yeşildere Sağlam, H., Başaran, F., \u0026Ccedil;etin Atay, E., \u0026amp; Şimal Yavuz, N. (2023). Turkish nurses\u0026apos; leadership orientations and clinical decision-making skills. \u003cem\u003eLeadership in Health Services, 36\u003c/em\u003e(3), 402-417.\u003cspan dir=\"RTL\"\u003e\u0026rlm;\u003c/span\u003ehttps://doi.org/10.1108/LHS-08-2022-0090 \u003c/li\u003e\n\u003cli\u003eWorld Medical Association. Declaration of Helsinki- Ethical Principles for Medical Research Involving Human Subjects 2015. Available from: http://www.wma.net/en/30publications/10policies/b3 (accessed 14 May 2023)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4427158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4427158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeadership is essential to nursing because it influences autonomy in nurses’ decision-making and work performance. Efficient leadership styles have been demonstrated to significantly influence patient outcomes, nursing practice, and organizational success. On the contrary, inadequate leadership can impede nurses' autonomy in decision-making, limit their job satisfaction, and ultimately hinder the quality of patient care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Aim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to evaluate the role of leadership styles in fostering work performance and autonomy in nurses’ decision-making at Northern Region Hospitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA descriptive exploratory design was used in this study with a sample size of 102 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results showed a linear relationship between Transformational leadership style and subjective measures of leader work performance and autonomy in nurses' decision-making. The study results indicated that most staff nurses defined their leader's style as transformational leadership, followed by transactional leadership and then laissez-faire leadership.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, Leadership must enhance and foster autonomy in nurses' decision-making ability. Leaders must improve their leadership style to positively impact autonomy in nurses’ decision-making and improve patients' healthcare. All managers down to the lowest channel must exercise a key leadership role to influence nurses to become good and independent leaders. This study involves nursing practice and nursing management. Nurse managers should receive training on the different leadership philosophies and how to apply the style that best advances the organization’s objectives.\u003c/p\u003e","manuscriptTitle":"Achieving Excellence: The Role of Leadership Styles in Fostering Work Performance and Autonomy in Nurses’ decision-making","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 20:31:48","doi":"10.21203/rs.3.rs-4427158/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84d97b05-629a-40eb-9de1-318cecf21b80","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-10T06:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 20:31:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4427158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4427158","identity":"rs-4427158","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}