A Systemic Model for Resilience and Time Management in Healthcare Academia: Application in a Dental University Setting

Featured Application: The Timebooster Academic Systemic Model (TASM) serves as a framework for the revolutionization of time management in healthcare academia. TASM integrates predictive analytics and systemic management principles, offering possible solutions to current time management challenges and offering a basis for future innovations in optimizing academic productivity and well-being. Abstract: The primary aim of this study was to provide a sustainable and systemic solution to the prevalent time management challenges within healthcare academia. The secondary aim was to explore the correlations between various factors influencing time management in a dental university setting. To achieve these objectives, a predictive model, the Timebooster Academic Systemic Model (TASM), was developed. This model was crafted through systemic analysis software and a systemic management approach, utilizing three fundamental pillars: teaching, research, and administrative tasks. Soft skill cultivation, automation implementation, the delegation of administrative responsibilities, and the role of resilient academic manager–leaders were also examined. The study found that prioritizing administrative tasks often consumes valuable academic time, resulting in excessive costs and lowered resilience levels among academicians. TASM proposes solutions such as soft skill cultivation, full automation, delegation, and the mitigation of time-consuming elements, emphasizing the role of resilient academic manager–leaders in enhancing collaboration and teamwork. Time management challenges significantly impact academic well-being and resilience. In conclusion, systemic models like TASM provide a valuable framework to address prioritization concerns, identify administrative obstacles, and manage low-value time-consuming tasks within academia in a systemic manner.


Introduction
In contemporary academia, the challenge of time management has become particularly acute as individuals address the complexities of multitasking and balancing diverse responsibilities within their professional and personal lives [1].Unlike conventional business commodities, time cannot be created, stored, or saved for future use, demanding a unique approach to its management [2].Leo Tolstoy's assertion in War and Peace that "The two most powerful warriors are patience and time" underscores the significance of timely decision-making in avoiding costly business mistakes.This imperative becomes even more pronounced in the volatile, uncertain, complex, and ambiguous (VUCA) current environment, where individuals, especially those in healthcare organizations, must work in a constant state of change [3,4].The demands placed on professionals in such an environment necessitate effective time management for the sustainability of their endeavors.Job demands, defined as the difficulties or challenges experienced in each role, play a crucial part in determining job satisfaction [5].However, the manageable nature of these demands influences individuals' satisfaction and success in their roles [6,7].They also cause heightened levels of anxiety, stress, and potential health issues [8][9][10][11].In response to these challenges, effective time management emerges as a pivotal process, involving strategic planning, realistic objective setting, and the efficient allocation of resources [12].Alan (2009) [13] emphasizes the importance of not only planning but also protecting scheduled time and adjusting others' expectations.
Within the healthcare academic community, predominantly occupied by academic professors responsible for teaching, mentoring, and research, the freedom to pursue passions is often compromised by financial constraints and workforce reductions post-pandemic [14][15][16].Midcareer academic professionals express dissatisfaction with their limited career development opportunities, current positions, and insufficient research time [17,18].More specifically, healthcare academicians, engaged in academic responsibilities, clinical training, and practice, face intensified time-related challenges [19].Despite recognizing the importance of time management, academic professors, especially women, struggle to balance their professional duties and personal lives [10,11,20].Balancing multiple roles, often extending beyond the standard working hours, further complicates time management [21][22][23][24][25].
However, efficient time management is vital, impacting productivity and satisfaction [26][27][28][29][30][31][32].Eliminating time-consuming tasks allows a focus on high-value activities, increasing productivity [33,34].This enhanced productivity then boosts self-satisfaction and motivation [35], while job satisfaction is closely linked to time control and organizational skills [32].Effective time management also leads to economic success, benefiting individuals and institutions [32].Moreover, reduced time wastage increases efficiency, potentially leading to greater financial gains [33,34].In today's job market, punctuality and organization are highly valued and often take precedence over technical proficiency [36].In academia, improved productivity enhances resource allocation and academic and financial sustainability [35].
Healthcare academicians face unique time management challenges due to their multifaceted roles [24,36].Balancing teaching, mentoring, research, and clinical duties, they work in a complex "ecosystem" where time is finite and critical.The integration of academic and clinical responsibilities introduces huge complexity [37].The unpredictable nature of clinical settings requires adaptability from healthcare academicians [38], while staying current with healthcare advancements adds to their time demands [39].In this dynamic environment, strategic time management is essential in maintaining academic rigor and quality clinical education [20].Further, healthcare academicians must develop adaptive approaches tailored to their unique challenges, ensuring that they effectively fulfill their diverse responsibilities while staying abreast of healthcare industry developments [40].
The primary aim of this study is thus to provide a sustainable and systemic solution to the prevalent time management challenges within healthcare academia by designing a systemic model with the use of relevant software.The secondary aim is to explore the correlations between various factors influencing time management in this model using the case of a dental university setting.

Time Management Approaches and Techniques in Academia
Strategic time management in healthcare academia empowers individuals to make autonomous decisions about their work schedules and task delegation [41,42], including active participation in meetings, taking periodic breaks, and prioritizing projects that align with their personal or professional development [43][44][45].Essential techniques include preemptive planning, list-making, and strategic time ordering [21,41,46].List-making aids task prioritization and delegation, while strategic time ordering supports extended focused work for rigorous research [36,47].Recognizing the need for breaks after major projects is also crucial [43].Focusing on projects that align with hierarchical progression or personal spiritual journeys and cultivating habits like limited distraction and singularity of purpose further enhance time management [44,48].Finally, using a month-long calendar for the planning and sequencing of activities with urgency codes improves productivity [21,41,46], while the psychological reward of completing tasks boosts motivation [49].
Research indicates that fewer than one in five employees use a proper time management system, with many relying on lists or email inboxes [50].The least effective technique is addressing tasks as they arise [51][52][53], while frequent email checking undermines productivity [50].As is already reported, the Eisenhower matrix helps to categorize tasks by urgency and importance, aiding in prioritization [33,54].Urgent tasks require immediate attention, important tasks need proactive planning, and non-urgent, non-important tasks can be eliminated or delegated [55,56] (Figure 1).Kamoun (2022) [57] notes the Eisenhower matrix's limitations and suggests task shuffling for better balance, promoting adaptability and a holistic view of task management [58][59][60].Moreover, perfectionism poses challenges by causing self-criticism, procrastination, and a reluctance to delegate, leading to burnout and reduced productivity [61][62][63][64][65]. Overall, from the relevant literature, it is derived that effective time management in healthcare academia involves a combination of strategic planning, prioritization, the use of frameworks like the Eisenhower matrix, and the management of perfectionistic and procrastination tendencies for healthier and more productive work practices.
systemic model with the use of relevant software.The secondary aim is to explore the correlations between various factors influencing time management in this model using the case of a dental university setting.

Time Management Approaches and Techniques in Academia
Strategic time management in healthcare academia empowers individuals to make autonomous decisions about their work schedules and task delegation [41,42], including active participation in meetings, taking periodic breaks, and prioritizing projects that align with their personal or professional development [43][44][45].Essential techniques include preemptive planning, list-making, and strategic time ordering [21,41,46].List-making aids task prioritization and delegation, while strategic time ordering supports extended focused work for rigorous research [36,47].Recognizing the need for breaks after major projects is also crucial [43].Focusing on projects that align with hierarchical progression or personal spiritual journeys and cultivating habits like limited distraction and singularity of purpose further enhance time management [44,48].Finally, using a month-long calendar for the planning and sequencing of activities with urgency codes improves productivity [21,41,46], while the psychological reward of completing tasks boosts motivation [49].
Research indicates that fewer than one in five employees use a proper time management system, with many relying on lists or email inboxes [50].The least effective technique is addressing tasks as they arise [51][52][53], while frequent email checking undermines productivity [50].As is already reported, the Eisenhower matrix helps to categorize tasks by urgency and importance, aiding in prioritization [33,54].Urgent tasks require immediate attention, important tasks need proactive planning, and non-urgent, non-important tasks can be eliminated or delegated [55,56] (Figure 1).Kamoun (2022) [57] notes the Eisenhower matrix's limitations and suggests task shuffling for better balance, promoting adaptability and a holistic view of task management [58][59][60].Moreover, perfectionism poses challenges by causing self-criticism, procrastination, and a reluctance to delegate, leading to burnout and reduced productivity [61][62][63][64][65]. Overall, from the relevant literature, it is derived that effective time management in healthcare academia involves a combination of strategic planning, prioritization, the use of frameworks like the Eisenhower matrix, and the management of perfectionistic and procrastination tendencies for healthier and more productive work practices.

The Timebooster Approach in Academic Time Management
Among the global challenges in academia, personalized time management strategies are vital, especially in the mid-career stages, requiring resilience and motivation [66,67].The "Timebooster approach" presented here is the basis of the proposed systemic model; it emphasizes strategic delegation to save time and optimize planning [68] through optimizing a leader role.Prioritizing tasks based on career evolution, incorporating enjoyable tasks, and integrating time management across all academic pillars are important [69][70][71].
Cultivating calmness, sustaining motivation, and strategic prioritization further improve time management in healthcare academia [72][73][74][75][76]. Setting boundaries to minimize interruptions, such as defining standard interaction times with students and personnel, preserves energy and well-being [77].Addressing timewasters like perfectionism and procrastination finally adds a spiritual perspective, fostering joy in academic pursuits and detachment from the results [78].These factors can be controlled with short educational experiences within academia from coaches, time management mentors, and mindfulness practices [79][80][81][82][83].Moreover, professional development programs, waste reduction initiatives, and process improvement programs ensure a balanced approach and enhance overall resilience [80][81][82][83].
Finally, word of mouth (WOM) significantly influences behavioral changes in education, promoting better well-being among students and educators.WOM facilitates the spread of personal experiences and endorsements, creating a sense of trust and authenticity that can effectively encourage positive behaviors and attitudes [84].In educational settings, peer recommendations and shared experiences about well-being practices, such as mindfulness, exercise, and time management strategies, can motivate others to adopt similar habits [85].Research indicates that WOM is particularly powerful in shaping attitudes and behaviors because individuals are more likely to act on information received from trusted sources within their social networks [86].Furthermore, WOM can reinforce the benefits of well-being initiatives, reinforcing a supportive community that values and practices holistic health approaches, thus enhancing the overall well-being culture in educational environments [87].

Modeling and Simulation
The systemic model presented in this study is referred to as the "Timebooster Academic Systemic Model-TASM".Its objective is to conceptualize and simulate the dissemination of the time management philosophy within healthcare academia, working on all factors addressed in the literature, as mentioned before.It is divided into several pillars of sustainability and is presented in Table 1.
Table 1.Description of the TASM pillars.

Role definition and delegation
Manager-leader role: Academicians adopt a manager-leader mindset, focusing on high-impact tasks and delegating non-expertise tasks to others.Strategic delegation: Use strategic delegation to save time and optimize planning, assigning tasks that do not require specialized skills to support staff [10,72].

Task prioritization
Eisenhower matrix: Use the Eisenhower matrix to categorize tasks as urgent and important, not urgent but important, urgent but not important, and not urgent and not important [54,55].Career evolution focus: Prioritize tasks that align with career goals and professional development [1,55,[73][74][75].

Time management techniques
Time blocking: Allocate specific time blocks for focused work on important tasks, minimizing interruptions [83].
Efficient documentation: Utilize efficient documentation tools to streamline administrative tasks and reduce time spent on paperwork [83].

Motivation and well-being
Incorporate enjoyable tasks: Infuse enjoyable activities into daily routines to sustain motivation and enhance well-being [73][74][75].Minimize interruptions: Set boundaries for interaction time to preserve personal energy and improve focus [81].

Addressing common timewasters
Perfectionism, procrastination, seeking no help, and keeping control: Recognize and manage perfectionistic tendencies and procrastination, start seeking help, and let go of constant control to enhance productivity [61][62][63][64]82].Spiritual perspective: bring joy in academic pursuits and detach from results to maintain a healthy work-life balance [82].

Professional development and education
Continuous professional development: Engage in ongoing professional development to stay current and improve skills [83][84][85][86][87]. Time management education: Integrate time management education into academic training programs to propagate efficient practices [83].

Technological integration
Educational technology: Utilize educational technology to streamline teaching and research activities [83].
Professional development programs: Implement professional development programs and waste reduction initiatives to foster resilience and well-being [87].

Implementation and evaluation
Process improvement programs: Gradually implement process improvement programs to ensure a balanced approach and maintain educational quality [84].Regular evaluation: Continuously evaluate and adjust the model based on feedback and changing needs to sustain academic performance and well-being [85][86][87].
To further explore the fundamental factors influencing time management in healthcare academia, we took data for all pillars available and relevant values from the Department of Dentistry of the National and Kapodistrian University of Athens, Greece.We used the program for the academic year 2023-2024 of the department to calculate the mean hours spent on teaching and clinical instruction for the mid-level academicians of the department.We also used the real feedback of five colleagues (a total of 63 members of the academic staff of the department), who consented to be interviewed by the same investigator, at the same office, and within a period of one week in February 2023, to give their estimation of the hours spent per pillar per week over this period.
Our hypothesis posits that academicians-specifically dentists in this context-embracing the time management philosophy described before will be influenced by colleagues who recognize its benefits and are either contemplating its implementation or are already practicing it in their academic roles.We anticipate varying responses, with some being informed about time management yet taking no further action, while others adopt the basic time management principles outlined in this study.Some may choose to implement a comprehensive time management program, fostering collaboration, prioritization, delegation, and a focus on their evaluation processes.We assume that academicians who are knowledgeable and proficient in time management will serve as influencers among their peers.
Drawing on the Socio-Ecological Model of Communication, which underscores the importance of understanding the intricate system within which the target audience operates [88], our model introduces an interaction factor that influences academic cooperation, providing a dynamic aspect to our system.The Socio-Ecological Communication Model for Social and Behavioral Change emphasizes the role of word-of-mouth (WOM) informa-tion transfer [89], which we believe will impact the dynamics of the systemic model.In our approach, we aim to shed light on the complexity, interdependence, and totality of the components within this complex adaptive system, avoiding the isolation of specific components from the larger system in which they are embedded.
In our study, we posit that the novel approach to time management in healthcare academia will be introduced to the faculty of the Department of Dentistry at the National and Kapodistrian University of Athens.The potential user base for this philosophy is equated to the total number of dental academic staff (N = 63).Initially, all potential recipients fall into the category of TimeOFF academicians, with a small subset designated as TimeON academicians (N = 6).We assume that TimeOFF academicians currently do not employ any fundamental time management principles.
At a given time, any academician falls into exactly one of the three states (Time-OFF_Academician, Motivated_Academician, TimeON_Academician) of the following state chart (Figure 2).The objective is to illustrate the evolving knowledge and practices of academicians over time, providing academia with insights into the dynamic influences of this phenom- Motivation for implementing the time management philosophy among TimeOFF academicians may arise voluntarily, through training programs such as time management seminars (time edu hours), or through influences from colleagues (word of mouth) and other influencing factors, both positive (academic level and support) and negative (timewasters).The adoption of changes will require both the need and time for these academicians to react (time to react).
Essentially, we posit that TimeON dentists (TimeON_Academicians) will serve as influencers, motivating TimeOFF dentists (TimeOFF_Academicians) to become motivated dentists (Motivated_Academicians), and finally adopt time management practices and become TimeON dentists (TimeON_Academicians).The degree of influence depends on the number of TimeON dentists relative to the total number of academic dentists and the extent of their interaction (contact) with TimeOFF dentists and other stakeholders, defining the adoption from WOM.The TimeON dentist, motivated dentist, and TimeOFF dentist teams form the accumulation points or levels in the model, while other factors either serve as system parameters ("constant") or play a parameter role in describing the phenomenon of time management in academia.
These parameters serve as indicators for • The number of academic dentists embracing time management practices; • The initial count of dentists integrating time management practices; • The anticipated progression of dental personnel maturation, representing an increasing percentage of the total potential users of the time management philosophy; • The average time needed for an academic professional to mature and adopt new time management practices.
The objective is to illustrate the evolving knowledge and practices of academicians over time, providing academia with insights into the dynamic influences of this phenomenon.The resulting benefits encompass: -Determining the timeframe for the complete dissemination of time management practices in academia; -Assessing the number of dentists within each subcategory, aiding in the planning of tailored educational support activities; -Identifying weaknesses in the dissemination of knowledge about time management; -Identifying and exploring factors influencing the spread of the Timebooster approach to time management.
In our model, the acceptance rate of the Timebooster time management philosophy is contingent upon the elements and the settings described in the following Table 2.

Stock Explanation Original_Tasks
The overall tasks created Rework_to_Do The tasks that must be redone TimeOFF_Academicians The number of dentists who do not apply time management TimeON_Academicians The TimeON academic dentists Completed_Tasks The overall completed tasks Undiscovered_Rework The undiscovered tasks to be performed Motivated_Academicians The motivated academic dentists

Dynamic Variable Explanation Productivity_Factor
The productivity factor Incoming_Tasks The tasks to be fulfilled for the total academic staff per time unit Motivation_Rate Both academic and WOM motivation Adoption_From_WOM The WOM adoption Motivation The academic motivation The number of initial tasks Uncompleted_Tasks_Factor The percentage of uncompleted tasks per time unit

Flow Explanation Progress
The completed tasks per time unit

Task_Rate
The tasks to be fulfilled of the total academic dentist staff per time unit Rework The discovered uncompleted tasks to be fulfilled per time unit Uncompleted_Tasks The uncompleted tasks per time unit

Transformation
The TimeOFF academic dentists who become motivated academic dentists per time unit

Trans2
The acceptance of the time management philosophy and the transformation of motivated dentists into TimeON dentists Rework_Discovery The discovered uncompleted tasks per time unit The causality diagram of the hypothesis is presented in Figure 3.

Task_Rate
The tasks to be fulfilled of the total academic dentist staff per time unit

Rework
The discovered uncompleted tasks to be fulfilled per time unit Uncompleted_Tasks The uncompleted tasks per time unit

Transformation
The TimeOFF academic dentists who become motivated academic dentists per time unit

Trans2
The acceptance of the time management philosophy and the transformation of motivated dentists into TimeON dentists

Rework_Discovery The discovered uncompleted tasks per time unit
The causality diagram of the hypothesis is presented in Figure 3.

Model Simulation
The model of the system that describes the phenomenon of the spread of time management for the Timebooster approach in dental academicians is implemented using the AnyLogic simulation software (AnyLogic Model, AnyLogic Model:

Model Simulation
The model of the system that describes the phenomenon of the spread of time management for the Timebooster approach in dental academicians is implemented using the AnyLogic simulation software (AnyLogic Model, AnyLogic Model: https://cloud.anylogic.com/model/4b05e7b8-0b4f-4b05-941d-1daf6801ae4b?mode=DASHBOARD) (accessed 20 January 2024).To simulate, the program requires the definition of some initial settings, namely the administration, education, research, academic support, time educ hours, academic level, workload, timewasters, adoption fraction, and contact rate.
The values in our model are the numerical values of the variables and the units indicate what is being measured by each variable.The behavior of the model over time is also examined using the software.A period of five years is chosen so that any results are evident.The choice of period can be changed through the model.
To monitor the evolution of the model over time, two tables of diagrams, Graphs1 and Graphs2, have been designed, and they are depicted in Figures 4 and 5.

Model Execution
The model is first executed with the help of the relevant software for the variable values referred to in Table 3 (Example 1), where the values were derived from the mean scores.We collected data from the calculations of time performed over the program and the interviews of the personnel, thus attributing to the factors specific percentages.

Model Execution
The model is first executed with the help of the relevant software for the variable values referred to in Table 3 (Example 1), where the values were derived from the mean scores.We collected data from the calculations of time performed over the program and

Model Execution
The model is first executed with the help of the relevant software for the variable values referred to in Table 3 (Example 1), where the values were derived from the mean scores.We collected data from the calculations of time performed over the program and the interviews of the personnel, thus attributing to the factors specific percentages.The tasks' progress over time is depicted in Figure 9.The tasks' progress over time is depicted in Figure 9.To demonstrate the importance of the WOM effect in the transition process and the effects on the tasks' progress, we executed the model for the values of the variables shown in Table 3, but we changed the contact rate from 0, as mentioned in Table 3, to 2 (Example 2).WOM is the positive effect of word-of-mouth information dissemination, which is supposed to be a spontaneous process among colleagues.In this sense, we tested the model, enabling different factors to be more evident according to the conditions of the group of dentist academicians in the Department of Dentistry, while the model was executed.The experiment is still ongoing.To test the reliability of the process, we conducted several experiments with different parameter values.
The resulting graphs are depicted in Figures 10 and 11 for Example 2.  To demonstrate the importance of the WOM effect in the transition process and the effects on the tasks' progress, we executed the model for the values of the variables shown in Table 3, but we changed the contact rate from 0, as mentioned in Table 3, to 2 (Example 2).WOM is the positive effect of word-of-mouth information dissemination, which is supposed to be a spontaneous process among colleagues.In this sense, we tested the model, enabling different factors to be more evident according to the conditions of the group of dentist academicians in the Department of Dentistry, while the model was executed.The experiment is still ongoing.To test the reliability of the process, we conducted several experiments with different parameter values.
The resulting graphs are depicted in Figures 10 and 11 for Example 2.
Appl.Sci.2024, 14, x FOR PEER REVIEW 13 of 23 To demonstrate the importance of the WOM effect in the transition process and the effects on the tasks' progress, we executed the model for the values of the variables shown in Table 3, but we changed the contact rate from 0, as mentioned in Table 3, to 2 (Example 2).WOM is the positive effect of word-of-mouth information dissemination, which is supposed to be a spontaneous process among colleagues.In this sense, we tested the model, enabling different factors to be more evident according to the conditions of the group of dentist academicians in the Department of Dentistry, while the model was executed.The experiment is still ongoing.To test the reliability of the process, we conducted several experiments with different parameter values.
The resulting graphs are depicted in Figures 10 and 11 for Example 2.   If we run the model for values presented in Table 3 and for its variation in Example 2, we have the graphs presented in Figures 12 and 13 where red and blue colors suggest differences over time in the execution of the model for the two cases.If we run the model for values presented in Table 3 and for its variation in Example 2, we have the graphs presented in Figures 12 and 13 where red and blue colors suggest differences over time in the execution of the model for the two cases.If we run the model for values presented in Table 3 and for its variation in Example 2, we have the graphs presented in Figures 12 and 13 where red and blue colors suggest differences over time in the execution of the model for the two cases.In Figure 13, a comparison is given for the TimeOFF academicians, the motivated academicians, the TimeON academicians, and the combination of all three categories.According to Example 1, using the values of Table 3, all TimeOFF academicians become TimeON academicians after 51 months, while, according to Example 2 (values A of Example 2) (Table S1), all Time OFF academicians become Time ON academicians after 16 months.We can observe the much faster transition of the academicians from the TimeOFF to the TimeON state, as well as the larger number of motivated academicians resulting from the contacts and the word-of-mouth effect.As a result, a higher peak can be observed in the original tasks as well as in the progress over time, as shown in Figure 13.
Next, we present some variations of the A values of Example 2, forming Example 3 of TASM's execution (Table 4).In Figure 13, a comparison is given for the TimeOFF academicians, the motivated academicians, the TimeON academicians, and the combination of all three categories.According to Example 1, using the values of Table 3, all TimeOFF academicians become TimeON academicians after 51 months, while, according to Example 2 (values A of Example 2) (Table S1), all Time OFF academicians become Time ON academicians after 16 months.We can observe the much faster transition of the academicians from the TimeOFF to the TimeON state, as well as the larger number of motivated academicians resulting from the contacts and the word-of-mouth effect.As a result, a higher peak can be observed in the original tasks as well as in the progress over time, as shown in Figure 13.
Next, we present some variations of the A values of Example 2, forming Example 3 of TASM's execution (Table 4).The comparison of the executions for the values of Table 4 is depicted in Figures 14 and 15.
Appl.Sci.2024, 14, x FOR PEER REVIEW 16 of 23 The comparison of the executions for the values of Table 4 is depicted in Figures 14  and 15.The comparison of the executions for the values of Table 4 is depicted in Figures 14  and 15.According to Table S2 (Supplementary Materials), using the B values of Table 4, all TimeOFF academicians become TimeON academicians after 16 months, while, according to Table S3 (Supplementary Materials), using the C values of Table 4, all TimeOFF academicians become TimeON academicians after 12 months.One may now observe the faster transition of the academicians from the TimeOFF to the TimeON state, as well as the larger number of motivated academicians resulting from the motivation effect due to academic factors.As a result, a higher peak can be observed in the original tasks as well as in the progress over time as shown in Figure 15, while there are fewer completed tasks due to the lower workload.
With these cost parameters, the pace of transformation for TimeOFF dentists will increase at a gradual rate, failing to complete the phenomenon within the study period of 60 months.This suggests that as administrative tasks and time-wasting activities peak, the rate of transformation slows, which aligns with expectations for academicians overwhelmed by extensive non-academic responsibilities.Various scenarios within the model demonstrate its dynamic nature, performing effectively based on the parameters provided.

Discussion
Our study explored the application of behavioral change management, communication principles, and practical methodologies like the Eisenhower matrix and the Timebooster approach within the healthcare academic context.The Timebooster Academic Systemic Model (TASM) offers insights that allow us to understand and address the behavioral dynamics of academic time management [24].The model emphasizes the importance of recognizing individual perceptions of time, establishing clear communication channels, and promoting collaborative problem-solving.These principles help academicians to coach themselves and others through word-of-mouth practices to effectively manage time complexities in the work environment [90].
One key aspect highlighted by TASM is the importance of recognizing the interconnectedness of individual behaviors and systemic factors within the academic environment, as mentioned elsewhere [91].Effective time management is not solely reliant on individual efforts but also on the organizational culture, support structures, and communication channels in place [24].Therefore, interventions aimed at improving time management must consider both individual behaviors and systemic influences, such as leadership styles, institutional policies, and communication norms [92,93].
Effective communication is essential for successful time management in healthcare academia, as shown by our model [71].TASM suggests that if we foster a culture of effective communication, healthcare academic institutions can cultivate an environment in which academicians feel supported, valued, and empowered to optimize their time management strategies.This emphasis on communication through WOM that the model suggests aligns further with research indicating that open channels for the expression of needs and management of change are crucial for staff well-being and program adaptation [24].Thus, effective communication stands as a cornerstone for success, aligning employees' goals with organizational purposes [94].Establishing frequent, friendly, ethical, and consistent workplace communications is essential in building resilience and addressing crises effectively as a team [17,[95][96][97].
Moreover, the Timebooster Academic Systemic Model (TASM) demonstrates the efficacy of integrating practical methodologies such as the Eisenhower matrix and the Timebooster approach within healthcare academia to enhance time management skills systematically [98].When researchers adopt a systemic approach, they can address the complexities of time management more comprehensively, as suggested by previous studies emphasizing the importance of systemic thinking in designing effective time management tools for the workplace [99].This holistic perspective allows for an in-depth understanding of the interconnections between various aspects of time management, facilitating the development of tailored strategies to improve productivity and well-being in academic settings.
In addition to individual-level interventions, the role of academic administrators in alleviating administrative burdens and promoting effective time management is crucial [100].Our model highlights the importance of this factor in time management initiatives in healthcare academia.Bozeman et al. (2020) [101] discuss the impact of computer-automated research grant management systems, or robotic bureaucracy, in organizing administrative processes and reducing the burden on academic staff.Automating tasks such as grant application processing, compliance monitoring, and reporting can significantly reduce time and effort, enhance efficiency, reduce errors, and improve overall productivity [101].Automation frees valuable time for academic staff, allowing them to focus on research, teaching, and knowledge dissemination.This not only benefits individual researchers but also advances scientific discovery and innovation [102,103].The adoption of automated systems in academia is therefore essential for a more efficient, productive, and supportive academic environment.Insights from TASM suggest further that academic administrators play a key role in shaping the organizational culture, establishing support structures, and implementing policies that facilitate efficient time management practices for the rest of the personnel [24].By delegating administrative tasks, providing training and resources, and creating a supportive work environment, academic administrators and automation can empower healthcare academicians to focus on their core responsibilities and achieve their professional goals [104].
As we report from the running of our model, implementing time management programs in healthcare academia requires meticulous systemic planning, resource allocation, and evaluation to address the challenges faced by academicians [17].These programs should aim to enhance efficiency and productivity in research, teaching, and administration, promising benefits like increased job satisfaction and academic performance [1,98].Successful execution depends on understanding the associated costs and challenges, necessitating strategic resource allocation and rigorous evaluation frameworks [20].Thus, institutions must invest in training, technology infrastructure, and administrative support [17,20,[102][103][104][105][106], with adequate financial and personnel resources to ensure smooth implementation and minimal disruption [107].Evaluation mechanisms are also crucial in assessing the impact of these programs on productivity and well-being, guiding informed decision-making and future investments [17,20].Overcoming challenges like resistance to change and technological barriers requires proactive measures, stakeholder engagement, and ongoing support to ensure the successful adoption and sustainability of time management initiatives through systems dynamics research [105,106].
Overall, the TASM model highlights the progression of time management evaluation in a healthcare (dental) academic setting.At this juncture, the lack of historical data for our model limits its capacity to fully optimize specific situations, and, thus, limitations exist.The model's effectiveness is partly contingent on the unique cultural and institutional contexts of individual academic environments, which may limit the generalizability of the findings across different settings.Additionally, the reliance on self-reported data may introduce biases, affecting the accuracy of the results.Future research should explore the longitudinal impacts of TASM on academic productivity and well-being and examine the model's applicability in diverse academic and healthcare contexts to control or measure the output and impacts of scholarly activities performed by academics in all sectors, which typically include teaching, research, publishing, and service to the academic community.Investigating the role of technology integration, such as automated systems for administrative tasks, and their influence on time management practices will also be essential.Further studies should focus on developing comprehensive evaluation systemic frameworks to measure the long-term effectiveness of time management interventions, considering both individual behaviors and systemic influences, such as leadership styles and institutional policies [92,93].

Conclusions
Systemic models like TASM provide a valuable framework to address prioritization concerns, identify administrative obstacles, and manage low-value, time-consuming tasks within academia.Effective time management in healthcare academia requires a multifaceted approach that integrates behavioral change management, communication principles, practical methodologies, and institutional support structures.TASM offers guidance in understanding the complexities of time management and fostering a culture of efficiency and well-being in healthcare academic institutions.It can serve as a predictive and strategic decision-making tool for the implementation of time management principles in healthcare academia.

Patents
The TASM model represents a patented systems-thinking approach to time management in healthcare academia.

Figure 2 .
Figure 2. Academicians' state chart.It depicts the different states that an academician can exist in.The arrows show the possible flows of the phenomena.These parameters serve as indicators for•The number of academic dentists embracing time management practices;•The initial count of dentists integrating time management practices;•The anticipated progression of dental personnel maturation, representing an increasing percentage of the total potential users of the time management philosophy;•The average time needed for an academic professional to mature and adopt new time management practices.

Figure 2 .
Figure 2. Academicians' state chart.It depicts the different states that an academician can exist in.The arrows show the possible flows of the phenomena.

Figure 3 .
Figure 3. Causality diagram of time management practices in the Department of Dentistry.

Figure 3 .
Figure 3. Causality diagram of time management practices in the Department of Dentistry.

Figure 4 .
Figure 4. Graphs1 for the monitoring of the evolution of the model over time.

Figure 5 .
Figure 5. Graphs2 for the monitoring of the evolution of the model over time.

Figure 4 .
Figure 4. Graphs1 for the monitoring of the evolution of the model over time.

Figure 4 .
Figure 4. Graphs1 for the monitoring of the evolution of the model over time.

Figure 5 .
Figure 5. Graphs2 for the monitoring of the evolution of the model over time.

Figure 5 .
Figure 5. Graphs2 for the monitoring of the evolution of the model over time.

Figure 6
Figure 6 shows the initial stage of the model execution based on the values of Table 3, while Figure 7 shows the final stage of model execution for a period of 60 months.All figures are the results of running experiments for different values of the variables in the AnyLogic cloud.The control panel interface enables the user of the model to change the values of the parameters that they are interested in and to see the behavior of the model over time before deciding to make changes to any of them.The end user of the model is anyone who wishes to predict the positive or negative results of any change that they wish to make and decide accordingly.

Figure 6
Figure 6 shows the initial stage of the model execution based on the values of Table 3, while Figure 7 shows the final stage of model execution for a period of 60 months.All figures are the results of running experiments for different values of the variables in the AnyLogic cloud.The control panel interface enables the user of the model to change the values of the parameters that they are interested in and to see the behavior of the model over time before deciding to make changes to any of them.The end user of the model is anyone who wishes to predict the positive or negative results of any change that they wish to make and decide accordingly.

Figure 6 .
Figure 6.The initial stage of model execution based on the values of Table 3.Figure 6.The initial stage of model execution based on the values of Table3.

Figure 6 .
Figure 6.The initial stage of model execution based on the values of Table 3.Figure 6.The initial stage of model execution based on the values of Table3.

Figure 7 .
Figure 7.The final stage of model execution for a period of 60 months, where all members of the personnel have become TimeON_Academicians.The academicians' transition from the TimeOFF_Academician state to the Moti-vated_Academician and the TimeON_Academician states over time is demonstrated in the graphs of Figure 8.

Figure 8 .
Figure 8.The academicians' transition from the TimeOFF_Academician state to the Moti-vated_Academician and the TimeON_Academician states over time.

Figure 7 .
Figure 7.The final stage of model execution for a period of 60 months, where all members of the personnel have become TimeON_Academicians.The academicians' transition from the TimeOFF_Academician state to the Moti-vated_Academician and the TimeON_Academician states over time is demonstrated in the graphs of Figure 8.

Figure 7 .
Figure 7.The final stage of model execution for a period of 60 months, where all members of the personnel have become TimeON_Academicians.The academicians' transition from the TimeOFF_Academician state to the Moti-vated_Academician and the TimeON_Academician states over time is demonstrated in the graphs of Figure 8.

Figure 8 .
Figure 8.The academicians' transition from the TimeOFF_Academician state to the Moti-vated_Academician and the TimeON_Academician states over time.

Figure 8 . 23 Figure 9 .
Figure 8.The academicians' transition from the TimeOFF_Academician state to the Moti-vated_Academician and the TimeON_Academician states over time.

Figure 10 .
Figure 10.Graphs showing the execution of the model for the variables in Example 2.

Figure 9 .
Figure 9.The tasks' progress over time.

Figure 9 .
Figure 9.The tasks' progress over time.

Figure 10 .
Figure 10.Graphs showing the execution of the model for the variables in Example 2.Figure 10.Graphs showing the execution of the model for the variables in Example 2.

Figure 10 .
Figure 10.Graphs showing the execution of the model for the variables in Example 2.Figure 10.Graphs showing the execution of the model for the variables in Example 2.

Figure 11 .
Figure 11.Graphs showing the tasks over time for the variables in Example 2.

Figure 11 .
Figure 11.Graphs showing the tasks over time for the variables in Example 2.

23 Figure 11 .
Figure 11.Graphs showing the tasks over time for the variables in Example 2.

Figure 12 .
Figure 12.Graphs for the comparison of the executions of WOM motivation in Example 1 and 2.

Figure 12 .
Figure 12.Graphs for the comparison of the executions of WOM motivation in Example 1 and 2.

Figure 13 .
Figure 13.Graphs for the comparison of the executions for the tasks of Example 1 and 2. Red and blue colors suggest differences over time in the execution of the model when there are changes in the factor of the analysis.

Figure 13 .
Figure 13.Graphs for the comparison of the executions for the tasks of Example 1 and 2. Red and blue colors suggest differences over time in the execution of the model when there are changes in the factor of the analysis.

Figure 14 .
Figure 14.Graphs for the comparison of the executions with the values of Table 4 for academicians.The red color corresponds to B Values and green to C values.

Figure 14 .
Figure 14.Graphs for the comparison of the executions with the values of Table 4 for academicians.The red color corresponds to B Values and green to C values.

Figure 14 .
Figure 14.Graphs for the comparison of the executions with the values of Table 4 for academicians.The red color corresponds to B Values and green to C values.

Figure 15 .
Figure 15.Graphs for the comparison of the executions with the values of Table 4 for tasks.The red color corresponds to B Values and green to C values.

Table 2 .
Parameters of the TASM model.

Table 3 .
Values used for the initial execution of the model.