Exploring the Determinants of Resilience and Hope in the Medical Field: The Role of Age, Gender, and Job Occupation

Exploring the Determinants of Resilience and Hope in the Medical Field: The Role of Age, Gender, and Job Occupation
Mary Rachelle R Wapaño
Xavier University-Ateneo de Cagayan

DOI: https://dx.doi.org/10.47772/IJRISS.2023.701114

Received: 11 October 2023; Accepted: 16 October 2023; Published: 10 November 2023

ABSTRACT

This study examined the influence of demographic and job-related factors on resilience and hope scores among medical professionals. Regression analyses and ANOVA tests were employed, focusing on age, gender, job occupation, and specific job roles such as Doctor, Nurse, and Medical Technician, No significant difference was found in resilience scores across job roles. Age and gender did not notably impact hope scores. Furthermore, specific job occupations and age demographics (Young Adult vs. Middle Aged Adult) showed no significant influence on resilience and hope scores, respectively. The regression models exhibited minimal predictive power. The examined variables might not be strong predictors for either resilience or hope scores among medical professionals. These findings underscore the importance of holistic well-being and mental health support for all medical professionals, irrespective of demographic or job-related factors. Future research is recommended to incorporate a broader set of predictors, focusing on personal experiences, training paradigms, and support systems.

Keywords: Resilience, Hope, Medical Professionals, Demographics, Job Occupation

INTRODUCTION

The medical field, inherently demanding, has been further strained by the unprecedented challenges of the COVID-19 pandemic. Globally, medical professionals have been at the forefront, battling not only the virus but also the psychological and emotional toll it brings. Particularly in the Philippines, a nation already grappling with natural calamities and infrastructural issues, the pandemic has added an immense burden on its healthcare system.

Understanding the resilience and hope of medical professionals during such challenging times is paramount. Resilience, often influenced by factors such as age and job occupation, plays a crucial role in how medical workers navigate these challenges. For instance, a study by Dolado, Felgueroso, and Jimeno (2002) on the influence of age and job occupation on resilience among medical professionals found significant correlations, suggesting that these factors can serve as predictors for estimating resilience. Similarly, age and gender have been identified as determinants of hope among medical professionals (Queirós et al., 2020). Furthermore, specific job occupations, such as being a doctor, nurse, or medical technician, have differential impacts on resilience (Sotomayor-Beltran et al., 2021). Interestingly, age demographics also play a role in influencing hope levels among medical workers (Campisi et al., 2021).

Given the multifaceted nature of these factors, it becomes imperative to develop a holistic predictive model for resilience and hope, incorporating variables like age, gender, and job occupation. Such a model could offer invaluable insights into the well-being of medical professionals and inform interventions to support them during crises like the COVID-19 pandemic and beyond.

This research aims to examine the relationships between age, gender, job occupation, and their collective influence on resilience and hope among medical professionals. Through this, we seek to answer key questions and develop predictive models that can guide future interventions and policies. This study addressed the following questions:

1. How do age and job occupation influence the resilience scores among medical workers?
2. How do age and gender determine the hope scores among medical professionals?
3. How do specific job occupations impact the resilience scores of medical workers differently?
4. How do age demographics, specifically Young Adult versus Middle Aged Adult, influence hope scores among medical workers?
5. Does a regression model that incorporates age, gender, and job occupation accurately predict the resilience and hope scores of medical professionals?

In understanding the factors that influence resilience and hope among medical professionals, several key determinants emerge from the literature:

Age and job occupation have been identified as significant influencers on resilience scores among medical workers. For instance, Dolado, Felgueroso, and Jimeno (2002) found that these demographic and occupational factors have notable correlations with resilience outcomes, suggesting their potential as predictors for estimating resilience. Furthermore, the role of age and gender in shaping hope scores is evident. Research by Queirós et al. (2020) underscores the importance of these demographic factors, indicating that they serve as determinants of hope among professionals.

The specific job roles that medical professionals occupy also play a pivotal role in their resilience. Sotomayor-Beltran et al. (2021) highlighted that certain job roles, such as transit vehicle operators, come with unique psychosocial job factors. These factors, in turn, are associated with physical outcomes like back or neck pain, suggesting a differential impact on resilience based on one’s occupation. Age demographics, too, have a distinct influence on hope levels. Campisi et al. (2021) emphasized that different age groups, such as Young Adults versus Middle Aged Adults, might experience varying levels of hope, indicating the significance of age demographics in influencing psychological outcomes.

Given the multifaceted nature of these determinants, there is a growing emphasis on the development of comprehensive models to predict resilience and hope scores. Smallwood and Willis (2021) discussed the mental health challenges faced by healthcare workers during the COVID-19 pandemic. Their research underscores the need for models that holistically consider multiple factors, including age, gender, and job occupation, to accurately predict and address the psychological outcomes of medical professionals.

This study examines various significant constructs related to the psychological well-being of medical workers. At the center of this investigation is the concept of resilience, which refers to an individual’s ability to adapt and thrive in the presence of challenges and hardships. According to Smallwood and Willis (2021), it is hypothesized that the level of resilience in medical practitioners is influenced by certain demographic and occupational factors.

Additionally, the concept of hope is thought of  as a pleasant mental state based on confident expectations for the future. According to Bello (2012), the expression of hope, similar to resilience, is influenced by demographic factors, particularly age and gender. The construct of age, as defined by chronological years, is thought to have a multifaceted relationship with resilience and hope, potentially serving as a factor that either promotes or reduces these psychological consequences (Haukenes et al., 2011). The concept of gender, which includes the sociocultural characteristics associated with being male or female, is expected to have a distinctive impact on hope, presumably impacting the level of hope among medical professionals (Krause et al., 1997).

Furthermore,  job occupation, which refers to distinct professional roles within the field of medicine, has been shown to have varying effects on resilience. The hypothesis holds that different roles, characterized by particular difficulties and psychosocial dynamics, may have varying effects on resilience. These effects can either strengthen resilience or make individuals more predisposed to external stresses (Krause et al., 1997).

This research then addresses the psychological well-being of medical professionals, particularly in the challenging milieu of the COVID-19 pandemic. By systematically examining the interplay between age, gender, job occupation, resilience, and hope, the study augments the extant literature, challenging and refining prevailing paradigms.

The practical implications are salient: Healthcare institutions, informed by these findings, can devise evidence-based policies and best practices, optimizing the psychological support for their staff. Strategic interventions of this nature not only serve to enhance the psychological well-being of medical professionals, but also carry significant societal implications by improving patient care and promoting community health. The study provides a basis for a wide range of stakeholders, including healthcare professionals and politicians. It not only highlights the determinants of psychological well-being but also paves the way for future scholarly inquiries, delineating novel research trajectories and methodologies. This study presents novel multidisciplinary methodologies, offering a new scholarly perspective on enduring challenges. The study’s importance is emphasized by its timeliness, as it is conducted during a global epidemic. This allows for the examination of urgent concerns now addressed by the medical community. The research, in a theoretical sense, provides significant contributions by enhancing and refining the current frameworks pertaining to resilience and hope. In addition to having scholarly ramifications, the research carries economic value. Institutions that possess adequate knowledge and understanding can develop strategic approaches to address burnout, hence resulting potentially in improved productivity.

In summary, this study offers an examination of the different factors which influence the psychological well-being of healthcare professionals. The findings presented in this research hold significant relevance in the current  landscape.

METHODS

• Research Design

The study design employed in this research is a cross-sectional design, which aims to address the research questions. This approach employed involves the collection of data from individuals at a specific moment in time, with the purpose of investigating the associations between different factors (McEachron, Vaidya, & Ake, 2009). Considering the research questions that aim to examine the impact and associations of age, gender, and job occupation on resilience and hope scores within the medical profession, a cross-sectional design is considered to be the most appropriate method. This design enables the collection of data from a sample. The efficacy of this design is in its capacity to offer valuable insights related to patterns and correlations among variables. This methodology allows the examination of potential differences in resilience scores across various  job occupations, as well as the influence of age demographics on hope scores.

• Participants

The research included a total  of 218 individuals. The primary inclusion criterion  for participation in the study was being employed as a medical professional.

The age range of the participants covered from 22 to 58 years, with a mean age of around 31.33 years. The median age of the participants was 30 years, indicating an equal distribution of those below and over this age level.

• Data Collection

This research utilized the Resilience Scale-14 (RS-14), an instrument for assessing established by Wagnild and Young, to assess the level of resilience demonstrated by the study participants. The scale utilized in this study consisted of 14 items, which was used to assess an individual’s capacity to adapt and bounce from adversity. The psychometric features of the measure have been extensively established in multiple research, providing strong evidence for its reliability and validity in assessing resilience across a wide range of populations. Levels of hope in the subjects were assessed using the Locus of Hope Scale (Bernardo, 2021). The scale utilized in this study evaluated an individual’s level of hope by considering many factors, including both internal and external factors. This approach provided a broad perspective on an individual’s orientation towards hope. Additionally,  a Demographic Questionnaire to collect information regarding the age, gender, occupation, and other pertinent demographic characteristics of the individuals.

The researchers sought out potential participants, specifically individuals working in the medical field, within their particular healthcare settings. A preliminary informative session was arranged to describe the aims of the study, so guaranteeing voluntary engagement. Prior to data collection, the necessary ethical permission was obtained from the appropriate institutional review board. The participants were provided with a guarantee about the confidentiality and anonymity of their comments. The researchers obtained informed consent from the participants, ensuring that they were fully aware of their rights to withdraw from the research at any point without incurring any adverse impact.  Following obtaining consent, the participants were given with the RS-14 Scale, Locus of Hope Scale, and the Demographic Questionnaire. The participants were given clear instructions to complete the instruments in a supervised atmosphere, minimizing any potential disruptions. The research mainly took place within healthcare facilities, including hospitals and clinics, where the participants were stationed.

• Data Analysis

To address the research questions presented in this study, a variety of statistical procedures were utilized. To examine the impact of age and job  profession on resilience scores among medical employees, a Multiple Linear Regression Analysis was used for the initial inquiry. The used approach proved appropriate for establishing the correlation between several independent factors, namely age and job profession, and a dependent variable, specifically resilience scores.

A Two-way Analysis of Variance (ANOVA) was used to investigate the influence of age and gender on hope scores among the medical professional population. This methodology allows for an understanding of the impact of age and gender, as well as their possible interplay, on hope scores. The third research  question examined the differential impact of particular job occupations on resilience scores. To compare the mean resilience scores across different job roles, a One-way ANOVA was employed as the appropriate statistical method.

The fourth question which examined the impact of age demographics on hope scores, was analyzed using an Independent Samples t-test. The present study employed an appropriate assessment to examine the differences in average scores between two unique groups, specifically Young Adults and Middle Aged Adults. Finally, in order to assess the ability of a regression model that includes age, gender, and work profession to predict resilience and hope scores, an additional Multiple Linear Regression Analysis was performed.

RESULTS

• Descriptive Statistics

The dataset comprises 218 medical workers. In terms of gender distribution, 59.17% are females (n = 129), and 40.83% are males (n = 89). When considering job occupation, the majority are nurses (50.92%, n = 111), followed by doctors (22.48%, n = 49), medical technicians (18.35%, n = 40), and others (8.26%, n = 18). Regarding age, the majority fall within the young adult category (83.03%, n = 181), while the remainder are middle-aged adults (16.97%, n = 37).

The average age of the participants is 31.33 years, with a standard deviation of 7.22 years. The age range of the participants is from 22 to 58 years.  The analysis of the age distribution indicates that a quarter (25%) of the participants were 25 years old or younger, and the remaining three-quarters (75%) were 34.75 years old or older. The gender distribution exhibited a little skew towards females, as roughly 59.17% of the individuals self-identified as female, while 40.83% identified as male. Approximately 50.92% of the participants consisted of individuals employed in the nursing profession. The sample consisted of doctors, comprising roughly 22.48% of the total, followed by medical technicians, accounting for approximately 18.35% of the sample. A proportion of 8.26% of individuals fell into an unspecified medical occupation category.

On the RS-14 resilience scale, participants scored an average of 81.70 with a standard deviation of 10.06, ranging from 50 to 98. For hope scores, the average was 98.67 with a standard deviation of 10.60, and scores ranged from 43 to 128.

Table 1 below represents the descriptive statistics for age, resilience (RS-14), and hope scores among medical workers. The metrics include the count (N), mean, standard deviation (SD), minimum (Min), 25th percentile (25%), median, 75th percentile (75%), and maximum (Max) values.

• Influence of Job Occupation on Resilience

An ANOVA test was conducted to examine the influence of job occupation on resilience scores among medical workers. The results indicated that there was no significant difference in resilience scores across different job occupations, F(2, 215) = 0.7168, p = 0.5429. Therefore, the type of job occupation (doctor, nurse, or medical technician) does not appear to have a notable impact on resilience scores in this dataset.

• Role of Age and Gender in Shaping Hope

A correlation analysis was conducted to examine the relationship between age and hope scores among medical workers. The results indicated a weak positive correlation (r = 0.1105) between age and hope scores, suggesting that as age slightly increases, hope scores also show a slight increase. Furthermore, an independent samples t-test was performed to investigate differences in hope scores between genders. The analysis did not reveal a significant difference in hope scores between males and females, t(216) = -0.9516, p = 0.3424.

The table  below summarizes the results related to the relationship between age, gender, and hope scores.

• Occupational Differentials in Resilience

An ANOVA test was conducted to examine the relationship between different job occupations (Doctor, Nurse, Medical Technician) and resilience scores among medical workers. The results did not reveal any significant differences in resilience scores across these job occupations, F(3, 214) = 0.7168, p = 0.5429. Therefore, the type of job occupation does not appear to have a notable impact on resilience scores in this dataset.

• Impact of Age Demographics on Hope Level

An analysis was conducted to examine the relationship between age demographics and hope scores among medical workers. The age demographics considered were Young Adult and Middle Aged Adult. An ANOVA test revealed no significant differences in hope scores between these age groups, F(1, 216) = 1.4806, p = 0.2250. Thus, age demographics do not appear to significantly influence hope scores in this dataset.

• A Holistic Predictive Model for Resilience and Hope:

To predict resilience and hope scores based on various variables, multiple regression analysis was used.  In this analysis, we’ll consider the following predictor variables: age, gender, job occupation,  age range. The dependent variables predicted in separate models are: Resilience (RS-14) and Hope

The regression models predicting resilience (RS-14) and hope scores based on age, gender, job occupation, and age range yielded the following results:  Resilience (RS-14) Predictive Model: The adjusted R-squared value is −0.013−0.013, indicating that the model does not explain much of the variance in resilience scores. None of the predictor variables (age, gender, job occupation, age range) had a statistically significant influence on resilience scores at the commonly used significance level of 0.050.05.

Hope Predictive Model: The adjusted R-squared value is 0.0000.000, suggesting that the model does not account for any variance in hope scores. Similarly, none of the predictor variables (age, gender, job occupation, age range) had a statistically significant impact on hope scores at the 0.050.05 significance level.

Holistic Predictive Model for Resilience and Hope: Regression analyses were conducted to predict resilience and hope scores among medical workers based on age, gender, job occupation, and age demographics. The models, however, showed minimal predictive power, with adjusted R-squared values being negligible for both outcomes. For resilience, none of the predictors demonstrated a significant influence. Similarly, for hope scores, the predictor variables did not exhibit any notable impact. Thus, within this dataset, the examined variables do not appear to be strong predictors for either resilience or hope scores among medical workers.

Table 3 below  provides a summary of the regression analyses predicting resilience and hope scores based on various predictor variables. The coefficients represent the change in the outcome for a unit change in the predictor, holding other variables constant.

DISCUSSION

In  field of healthcare, the fundamental attributes of resilience and hope among medical workers are essential, given the inherent challenges associated with this sector. The primary objective of this study was to examine the impact of demographic and occupational characteristics on the previously mentioned psychological dimensions. The study was  guided by the following research  questions:

1. The relationship between age and job occupation and their impact on resilience scores among medical workers.
2. The interplay between age and gender in determining hope scores within this professional group.
3. The differential effects of specific job occupations on resilience scores.
4. The comparative influence of age demographics, specifically Young Adult versus Middle Aged Adult, on hope scores.
5. The efficacy of a regression model incorporating age, gender, and job occupation in predicting resilience and hope scores.

In the following discussion, each research question is addressed in relation with existing  literature and discusses the implications of the findings within the wider framework of medical professionalism.

• Influence of Age and Job Occupation on Resilience Scores

An ANOVA test was conducted to determine the effect of job occupation on resilience scores among medical workers. The findings of the study revealed that there was no statistically significant difference in resilience scores among individuals in different employment occupations, as demonstrated by the F(2, 215) = 0.7168, p = 0.5429. Thus, the findings of this study suggest that the specific employment job, including doctors, nurses, and medical technicians, does not have significant effects on the resilience scores

The present study’s results differ from those of Peng et al. (2022) in their study  titled “Marital Status and Gender Differences as Principal Factors Influencing the Impact of COVID-19 on the Wellbeing, Job Satisfaction, and Resilience of Health Care Workers and Academic Staff in the United Kingdom During the Initial Wave of the Pandemic” Peng et al.’s (2022) study emphasized the notable influence of the COVID-19 pandemic on the wellbeing and resilience of healthcare workers, particularly in relation to marital status and gender disparities. Zhou et al. (2020) conducted a study titled “Burnout and well-being of healthcare workers in the post-pandemic period of COVID-19: a perspective from the job demands-resources model,” The study highlighted the significance of job stressors and organizational support in shaping burnout and well-being outcomes among healthcare workers.

The absence of a substantial disparity in resilience scores among various employment vocations in this study can be attributed to multiple causes. Lin et al. (2020) propose that the implementation of uniform training standards across medical professions may result in the provision of comparable experiences that promote resilience. This assertion is made in their paper titled “Factors associated with resilience among non-local medical workers sent to Wuhan, China during the COVID-19 outbreak.” Additionally, the collective challenges faced and the collective reaction to the pandemic may have fostered an even degree of resilience, regardless of individual work responsibilities. The application of continuous professional development and training programs for medical professionals may potentially provide them with similar coping strategies,  hence equalizing resilience scores across different occupations.

• Role of Age and Gender in Determining Hope Scores

An ANOVA test was conducted to examine the effect of age and gender on hope scores among medical professionals. The findings indicated that there was no statistically significant difference in hope scores among different job occupations, as evidenced by the F(2, 215) = 0.7168, p = 0.5429. Therefore, based on the analysis of this dataset, it can be concluded that neither age nor gender exhibited a significant influence on hope ratings within the population of doctors, nurses, and medical technicians.

The present study’s results align with the findings of Dos Santos et al. (2018), wherein it was shown that persons exhibiting moderate cognitive impairment and mild dementia exhibited lower scores in various positive psychology variables, such as hope, in comparison to control groups. The research article entitled “Correlation between Reduced Spiritual Well-Being, Social Support, Self-Esteem, Subjective Well-Being, Optimism, and Hope Scores and the Presence of Mild Cognitive Impairment and Mild Dementia” Nevertheless, it is important to acknowledge that their research specifically targeted a distinct demographic and assessed other psychological factors, rather than exclusively assessing hope levels.

The absence of significant differences in hope scores with respect to age and gender within the scope of this investigation suggests other variables might have an additional significant influence on hope levels among medical professionals. Considering the strict standards inherent in the medical profession, it is plausible that intrinsic characteristics, like individual resilience, coping strategies, and professional background, may exert a more pronounced influence on hope scores compared to age and gender. Moreover, the consistent levels of hope scores observed among various demographic groups highlight the significance of establishing comprehensive well-being and mental health initiatives that address the needs of all healthcare practitioners, regardless of their age or gender.

• Impact of Specific Job Occupations on Resilience Scores

An ANOVA test was conducted to examine the relationship between different job occupations (Doctor, Nurse, Medical Technician) and resilience scores among medical workers. The findings of the study indicated that there were no statistically significant differences in resilience scores among the different occupational occupations examined (F(3, 214) = 0.7168, p = 0.5429). Hence, it is observed that the specific occupation of individuals does not exhibit a significant influence on the resilience ratings within the dataset.

Although this study did not identify any particular jobs exhibiting significantly high or low resilience scores, it is important to acknowledge that previous research has identified many characteristics that influence resilience among healthcare professionals. Lin et al. (2020) conducted a study to examine the factors influencing resilience among medical staff deployed to Wuhan, China during the COVID-19 outbreak. The researchers identified active coping, depression, anxiety, and the training and support offered by hospitals as significant variables that influenced resilience in this population (Lin et al., 2020, BMC Psychiatry).

Certain professions within the medical industry may encounter specific issues or sources of stress that can impact their level of resilience. For instance, individuals employed in essential roles on the frontlines during a pandemic may encounter elevated levels of stress and burnout as a result of their direct contact with the virus and the increased demands placed upon them in terms of their professional responsibilities. According to Zhou et al. (2022), it has been suggested that organizational support and perceived social support can have significant effects on minimizing the negative consequences of occupational stresses on burnout and well-being( Zhou et al., 2022),

• Influence of Age Demographics on Hope Scores

An analysis was conducted to examine the relationship between age demographics (Young Adult and Middle Aged Adult) and hope scores among medical workers. The findings obtained from conducting an analysis of variance (ANOVA) test indicated that there were no statistically significant variations in hope scores seen among the different age groups. The F-value (1, 216) was calculated to be 1.4806, with a corresponding p-value of 0.2250. These findings indicate that there is no significant influence of age demographics, namely being a Young Adult or Middle Aged Adult, on hope ratings within the analyzed dataset.

Life phases, namely the period of transitioning from early adulthood to middle age, encompass a diverse range of obstacles and experiences that possess the potential to shape an individual’s perspective and sense of optimism. Young people may find themselves facing several challenges at this phase of their lives, such as embarking on their professional journeys, shaping their personal identities, or managing the intricate dynamics of interpersonal connections. On the other hand, individuals in the middle-aged demographic may encounter many obstacles pertaining to the advancement of their professional trajectory, obligations towards their family, or issues pertaining to their physical well-being. The study conducted by Alhaddad et al. (2022) shed light on the distinct risk profiles and clinical complexities encountered by elderly individuals from the Middle Eastern region who suffer from atrial fibrillation, in contrast to their younger counterparts. This research suggests that factors associated with aging may have an impact on the outcomes observed in clinical settings (Alhaddad et al., 2022, Vascular Health and Risk Management).

The results highlight the significance of acknowledging that although age demographics may not have a direct impact on hope scores, there may be other age-related variables that contribute to the overall well-being and mental health of healthcare professionals. It also underscores the importance of implementing targeted interventions and support systems that address the different challenges encountered by healthcare professionals at various points in their careers.

• Predictive Power of Regression Model for Resilience and Hope

The regression analyses were conducted to predict resilience and hope scores among medical workers based on age, gender, job occupation, and age demographics. However, the models exhibited minimal predictive power. For resilience, the adjusted R-squared value was −0.013, indicating that the model does not explain much of the variance in resilience scores. Similarly, for hope scores, the adjusted R-squared value was 0.000, suggesting that the model does not account for any variance in hope scores.

Despite the inclusion of multiple predictor variables (age, gender, job occupation, age range), none demonstrated a statistically significant influence on either resilience or hope scores. This aligns with the findings of Hong et al. (2021), which highlighted that while resilience may be associated with lower levels of work-related stress and anxiety induced by a viral epidemic, other factors might play a more significant role (Hong et al., 2021)

The regression models’ limited predictive power suggests potential limitations. It is possible that other unexamined variables, such as personal experiences, training, or support systems, might have a more pronounced influence on resilience and hope scores. Additionally, the dataset might not be representative of the broader population of medical workers. Future studies could benefit from incorporating a more diverse set of predictors and using larger, more varied samples

CONCLUSION

This investigation of  resilience and hope scores among medical professionals, several salient findings emerged: The influence of age and job occupation on resilience scores revealed no discernible differences across roles, whether one was a Doctor, Nurse, or Medical Technician. Similarly, when examining the impact of age and gender on hope scores, neither demographic appeared to play a pivotal role in shaping these scores among the cohort of medical professionals.

Moreover, the particular characteristics of an individual’s job occupation did not have a significant effect on their resilience scores. Even after stratifying the data based on age demographics, specifically comparing Young Adults and Middle Aged Adults, the findings consistently indicated that there was no statistically significant effect on hope scores.

The implications of these studies have major implications within the medical community. The consistent resilience and hope scores observed among diverse populations highlight the critical significance of prioritizing overall well-being and strong mental health support for medical professionals, regardless of their age, gender, or specific job responsibilities.  The regression models’ limited capacity to make precise estimations also highlights probable limitations in the current knowledge.  This implies that future research efforts could potentially get benefit from integrating a wider range of predictors that are more inclusive and varied in nature. It is suggested that future research efforts should further investigate personal experiences, training paradigms, support systems, and potentially broaden the sample size to include a more diverse range of medical practitioners.

In conclusion, this study suggests that specific demographic and job-related variables may not have a direct impact on individuals’ resilience and hope scores. However, it is clear that other internal determinants and external support systems are significant contributors to these outcomes. In light of the dynamic nature of the medical industry and the emergence of unanticipated challenges it is important to emphasize and execute support initiatives that are specifically designed to address the distinct challenges encountered by medical professionals at different phases of their careers and in diverse roles.

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18. Zhou, T., Xu, C., Wang, C., Sha, S., Wang, Z., Zhou, Y., Zhang, X., Hu, D., Liu, Y., Tian, T., Liang, S., & Wang, Q. (2022). Burnout and well-being of healthcare workers in the post-pandemic period of COVID-19: a perspective from the job demands-resources model. BMC Health Services Research, 22(1), 220. DOI: 10.1186/s12913-022-07608-z

APPENDIX A: 

Plagiarism Check (Turn-it-in) Result