Migrants’ sense of belonging in their country and community of residence has direct effects on their health and wellbeing. A diverse set of case studies suggest that legal immigration status plays a primary role in shaping migrants’ opportunities for and experiences of belonging. Few of these studies, though, have examined belonging for migrants with varied legal immigration statuses living in the same receiving context, limiting our understanding of if and how migrant status interacts with other factors to shape access to belonging for migrants settling in the same host community. To address this gap, we analyze 73 semi-structured interviews with migrants in Utah, USA, to investigate the process and experience of belonging for migrants across permanent, temporary, undocumented, and refugee statuses. While legal immigration status is an important factor shaping (non)belonging, it does not appear to function as a master status for migrant belonging. Rather, we find that legal immigration status works alongside a number of community-level factors—including cultural, social, linguistic, and racial/ethnic factors—to shape belonging for migrants of all immigration statuses. These non-legal, community-level factors emerged as critical features of (non)belonging for many migrants living in Utah. Our findings suggest that, although they cannot change federal immigration policies, local- and state-level governments and organizations can enhance migrants’ access to belonging and wellbeing across many other dimensions. What does it mean to belong to a community, and why does it matter if someone feels like they belong? The migrant experience can reveal the contours of belonging in a specific society in ways that illuminate the often taken-for-granted formal and informal social structures of everyday life. Belonging also plays an important role in shaping migrants’ (and non-migrants’) health and wellbeing ,2. ReseaAs a theoretical concept, belonging involves civic, social, and emotional dimensions ,11. Migrin the same setting—refugee, permanent, temporary, and undocumented migrants in Utah. Through this analysis, we are able to consider the salience of permanent, temporary, and undocumented migrant classifications in shaping belonging when compared with other key community-level cultural, social, linguistic, and racial/ethnic factors.As important as this literature is for understanding migrant belonging, it is hard to pinpoint how migrants experience belonging across a host of case studies with different contexts and subjects. Our contribution, then, is to compare the experiences of multiple migrant groups To do so, we utilize 73 semi-structured interviews with migrants living in Utah, collected in 2017–2018 (refugees) and 2019–2020 . Utah has received tens of thousands of refugees for resettlement over the past two decades, and its immigrant population has increased more than 350 percent in the past 30 years , Utah reBelonging can be difficult to define, both conceptually and affectively . BelongiScholars have identified factors that are positively and negatively associated with a sense of belonging . FactorsOur approach to understanding belonging has an important framing that is often underutilized in the literature: we focus on belonging as defined by the respondents themselves ,43. Thisliminal inclusion, wherein individuals (such as students or workers) have temporary or circumscribed rights to live, study, and/or work in the United States for the duration of their visas, without a path to permanent residence or citizenship  In order to identify qualifying interviews, we conducted a term search for the word “belonging” in all 162 interview transcripts. All interviews found to include the term in the interview transcription were included in our analysis. From our search, it is clear that this difference in thematic interview content reflects the interviewers’ questions and not any meaningful difference among the study participants. We found no evidence that the interviewees whose responses were not analyzed here have distinctly different understandings or experiences of integration, community closeness, belonging, etc. from those whose responses were included in our analysis. Migrants with permanent status (38 interviewees) are those migrants who possess lawful permanent residency—a status which grants them long-term permission to live, work, and study in the US—or have transitioned from permanent residency to citizenship. A majority of permanent migrants access this status through familial relationships with a US citizen spouse, parent, or sibling  of a great, big country” . The importance of permanent status for belonging can be seen in Sebastian’s description of the elusive nature of belonging for him as an undocumented immigrant:I’ve always had to go above and beyond to prove that I belong. One day I want to actually be a citizen, you know? [starts crying] And I, and I think that that will probably be the most important day of my life, you know? Cause it’s a chance to finally say like, “Look, I really do belong.” I could live anywhere here and know that this was my home because I’ve worked so hard to belong here.Knowing one has permanent status within the US signals belonging to migrants and non-migrants alike, acting as official evidence of membership and, thus, belonging.Permanent resident status also provides specific legal rights. Naomi (temporary status) said she would be able to feel like she had a place if she had “similar rights [as] people from here” and this would allow her and others to say, “this is your country, and that, you know, you’re from the United States.” Wangmo (refugee), recounted how citizenship gave them “somewhere to belong”, and Sonam (refugee) expressed that citizenship gave her “some place to call home.”But as Cultural practices and connections played a profound role in shaping belonging for many interviewees.  Participants in our sample had a number of explanations for finding cultural belonging. Perhaps because culture is complex (and hard to explain) and each migrant has a unique culture (composed of multiple elements), interviewees spent the most time explaining how elements of culture, and their familiarity with those elements, shaped their sense of belonging. Migrants specifically described finding belonging through cultural familiarity with and embracing elements of national and local culture, religious participation , and engaging with co-ethnic communities.Some interviewees described how they see taking part in the general “American culture” as a signifier of integration. Sebastian, an undocumented immigrant who came to the US as a child, explained why, despite his lack of citizenship, he thinks of himself as American:I feel like I’m an American. Yeah. Right. But like the arbitrary designation of citizen is… I’ve never even felt like remotely close to that, you know? Right. But I definitely feel American, you know, like I grew up in the same high schools. I watched the same movies growing up in the same language. I played the same video games. I watched the same TV shows every Saturday morning. I read the same comic books and, like, I think I went to the same goddamn church. I feel like there is nothing that separates me from my American friends, you know?Sebastian has been participating in what he regards as the “typical” American life since he was little. Because he is doing things that those around him are doing, he does not see himself as different. Rather, he sees himself as belonging culturally, even if he does not belong legally.Other participants such as Andres (temporary status) described belonging in cultural terms: “being able to speak English, […] enjoying the food because everybody eats, […] sporting events, […] just like going and being part of the crowd.” Irene (permanent status) also regards herself as belonging because “in my house, the language spoken is English, the food that is eaten is typical food from here, the things we do are typical American.” In these ways, having cultural proximity to or participating in the dominant culture can shape migrants’ integration and sense of belonging, regardless of their legal migration status.Not all migrants who find cultural belonging completely immerse themselves in the host culture, seeking instead to strike a balance between their home and host cultures. Many of these migrants find their multicultural approach as a pathway to belonging. Alfonso (permanent status) “took some aspects of their [US] culture, but the core is still me;” similarly, William (permanent status) believes that “for integration, I do not think it is losing your cultural heritage but it’s coming into America and taking what you think is best and adopting those things.” Cristina (permanent status) expressed a desire to not only merge cultures in her own life, but also in the lives of her children: “I want to keep my culture and teach my kids my culture. [...] I want them to learn the culture of the United States, but I really want to keep what makes me feel that I am here, but I am from there.” Yu Yan (temporary status) shared similar sentiments when she explained that it is important:To have the ability to build your own culture. Shape the person you want to become. Also hold onto those values you have for yourself. As immigrants we come with our own culture and background. You are constantly evolving and changing. Every day you are immersing yourself with so many different cultures that may be different or hard.Like Yu Yan, some migrants see the importance of understanding and participating in their host country’s culture while also maintaining their home country’s culture as a way to facilitate a sense of belonging.Other migrants shared that their sense of belonging was tied to co-ethnic community membership and activity. These migrants were able to find a big enough part of their “home” culture in the US to feel like they belong here. Noelia (permanent status) was able to preserve her identity within the greater community by intentionally seeking out community among the Mexican population in Utah:When we moved back from Mexico a few years ago, we started going to our English “ward,” our church congregation that was English-speaking. But I really wanted to be in a Spanish ward. And a couple of years after, we moved to the Spanish ward because that’s where my husband was assigned to work and serve, and I felt right at home there. So, at church, surrounded by Hispanics, or Spanish speakers, is one of those places where I love it. But also, when I surround myself with women who can also relate to my heritage, whether it’s because they speak Spanish or because they too have Hispanic heritage, that too has made me feel right at home. And I hadn’t realized that was one of the things I needed. Like, I have a ton of friends who are English speakers, and I love being with them, but I hadn’t realized how much—how I am my complete self when I can be Spanish Noelia.This completeness of self that Noelia described is something other interviewees sought by finding ways to preserve their identities. Some recounted their efforts to maintain a smaller community within their new host community, like Carla (permanent status), who explained that “in our case, in my house so to speak, we live like [we’re] in Ecuador. We cook like we’re in Ecuador, we always speak Spanish, we try to have decor that reminds us of our country. So, we conserve our own values.” By finding ties to their home country’s social and cultural networks, some migrants are able to maintain a sense of belonging even within larger and mainstream US culture, like Beth (permanent status), who says of the Filipino community in Utah that “every time I’m with them, it feels like home.”Another aspect of cultural belonging for some migrants in Utah stems specifically from the state’s unique religious context. Approximately three-fifths of the interviewees were members of the Church of Jesus Christ of Latter-day Saints (LDS Church), the majority religion in Utah whose leaders founded the state and to which more than half of Utah’s population claims affiliation . , for example, was not familiar with “all of US culture, exactly, but Utah culture” and she “got immersed into the BYU culture, which was even more kind of in a bubble, restricted and different. I liked it though. I mean that I felt comfortable in that kind of environment.”  Eva (temporary status), before moving to the US, knew “BYU and BYU Idaho were religious schools, based on the values we have at church. The new lifestyle didn’t impact me the way I thought it would. It was not a huge change.” Knowing the LDS culture allowed Emilia and Eva to comfortably participate in this predominant Utah subculture as soon as they arrived in Utah.Not all migrants had this religious familiarity upon arrival or experienced religious belonging in Utah. Many participants joined the religion after settling in Utah, and most of the refugee participants had not heard of the LDS Church prior to migrating to Utah. For migrants unfamiliar with the LDS Church, confronting this unique Utah subculture leaves some feeling like “double”  outsiders ,96. The Relationships and social networks facilitate belonging by providing encouragement for migration, giving migrants access to resources upon arrival, acting as “evidence” of belonging, and giving migrants a community to which they can belong. Many migrants establish social networks with US-based family and friends before they migrate to the United States. A number of interviewees had relatives who already lived somewhere in the US. Some had not “seen [their] sister in a long time” , “were separated for three years from [their] dad” , “already had family here” , had “parents [who] came first” , or “had some family in town” . Other respondents, particularly those affiliated with the LDS Church, had friends through international (religious) social networks who inspired or helped them to come to the US, and often to Utah specifically. For these interviewees, their international social networks motivated and even facilitated their international migration, even when the interviewee did not have clear legal or economic avenues that would help them migrate successfully.Upon and after arrival, migrants’ US-based friends and family can help them find needed resources. Alejandra (permanent status), for example, attributes her success at work to her relationships: “In regard to working, it has all been thanks for networking with friends from school. I have kept that contact with people in my field. I have had opportunities.” Friends may provide short- or long-term opportunities that allow migrants to participate in society. Families also often provide the resources and support migrants need. Eva (temporary status) said, “I think it has a lot to do with family—they are the ones I call when I need help.” Other acquaintances and connections offer the support needed as migrants arrive, helping migrants “fit in” , experience “growth” , “communicate” and “improve” (Desi-permanent status), and find an “instant connection” .Often relationships themselves are what help a migrant feel like they belong, as many interviewees described having friends and other relationships as evidence of belonging. Being able to make friends was seen by many interviewees as a rite of passage. Everyone else around them had a social network  and once they had one of their own, it meant they also belonged. For Andres (temporary status), having American friends and living a daily life like theirs is evidence of his belonging: “I have friends. I go to school, and I just live a normal life, like any other American-born teenager.” Charlotte (temporary status) feels similarly, observing: “I’m just here having fun, making friends...why wouldn’t I belong here?” For Lexi (permanent status), this belonging can happen even without seeking it: “I think that when you are working and going to school somewhere, even if you don’t want to integrate, like those things integrate you because you go to school, you learn […]. You’re spending time with those people and that, it just starts to rub off on you.” As migrants are able to fulfill social expectations of mainstream US society, like making friends, they increasingly feel a part of US society. Overall, having social networks is an important facilitator of belonging because having friends allows migrants to find similarities with others around them, rather than emphasize their differences.Some migrants, however, do not have strong social networks and, for many of them, their lack of social connections and interactions can hinder their sense of belonging. Without strong relationships, many migrants do not have full access to their communities or the resources within them. Furthermore, their lack of connection to the broader community may lead them to feel like they do not belong. Abdul (permanent status) believes that belonging is related to how strong of a connection you have with the community. He reflected on the importance for him and other migrants to “gain inroads into the local community or some of the community or some of the person[s].” Acknowledging that he is not “a gregarious person,” Abdul explained that he struggled to build connections that helped him feel like he belonged. When not able to have social interactions with others, migrants may feel a lack of connection to the community. As Isaro (refugee) described: “It’s kind of my fault I didn’t get involved when I was [in college] but, I don’t know. It was just hard to like to connect. And I don’t know, maybe I feel like, ‘cause, well, like I’m different…Before I join anything, I have to find this little connection.” For many interviewees, building strong social relationships is key to creating a sense of belonging, even if they, like Lusamba (refugee), do not feel it yet: “Since I came here, I didn’t belong. I want to be friendly with different people so I can learn from them, [and] they can learn from me. That’s helping me to adjust”.Many interviewees identified mastery of the English language and the ability to communicate with others as another important facilitator of belonging. Being able to communicate well with others helped many confident English-speaking interviewees feel like they belong. Li Jun (temporary status) had a familiarity with English before migrating because he had attended American schools abroad. Given this linguistic fluency, Li Jun concluded that, “for the most part, I could integrate easily because there was no language barrier”. Sam (permanent status) shared similar thoughts about the need for mastery of the English language. He said, “I think at the basic level, integration is getting to the point where you can function in a society. And that may mean different things. I think learning the language is a big part of that. I think it’s hard to integrate fully if you don’t know the language because you’ll always be at a disadvantage if you can’t do that”.Migrants who have not yet mastered the language often feel a sense of alienation. Raul (permanent status) shared, “If someone hears me doing something in Spanish, they try to intimidate me and question what I came to do in this country and tell me I must speak English like them. I tell them I’m learning; to be patient”. Phil (permanent status) also felt a sense of pressure to learn English, when he said: “I feel like moving here and understanding like, I should probably speak English, you know, people expect me to speak English.” Dayana (permanent status) recognized that her lack of ability to communicate was “the most challenging” aspect of trying to fulfill her goals in Utah, and it made her feel like she did not have a voice. This kind of alienation experienced by those who did not fully master the English language hindered their belonging, even when they held a permanent legal status.Interviewees often explicitly mentioned race when speaking of their belonging (or lack thereof), with regard to both race-based discrimination and a lack of diversity in many Utah communities. For interviewees perceived as non-white, their race and ethnicity were noticed by those around them and has been used by others as a mechanism to exclude them in formal and informal interactions. When Diego (permanent status) passed through customs at the airport, he was asked whether he had enough money to travel, and when answering that his family owned a hotel, the officer asked him whether it was a “two-room hotel full of cockroaches.” Mandy (permanent status) noted that when she would go to “other people’s houses and they’ve never seen, like, a Hispanic in their home or something and it might have made me feel like they were kind of scared that I was just going to do something”. Similarly, Alfonso (permanent status) went on a hike with friends and noticed there “was this white guy who was so annoyed I was there, and I didn’t know why. Come to realize he didn’t really like Hispanics that much.” Carolina (permanent status) has been told to “go back” to her country of birth, even after being in the US for over two decades. For many interviewees, these racially exclusionary experiences (which were not isolated events) made them feel like they did not, or could not, belong.A lack of racial diversity in Utah also affected the way that migrants saw their belonging. When Diego (permanent status) first arrived at his Utah elementary school, he realized he “was the only brown kid. I was the only colored kid in the whole school.” Li Jun (temporary status) noted that there “aren’t many Asians in Utah and it was easy to be singled out as the only Asian in class.” Interviewees with lighter skin or hailing from countries that are considered “whiter” experienced racial inclusion, like Alejandra (permanent status), who noted that “because I am fair, my skin is light, I have not experienced that same discrimination [as my] peers from the same country.” Georgi (permanent status) expressed a similar sentiment, describing that, in his interactions with US-born Americans “everybody was nice to me. Sadly, I think it’s because I was an immigrant from Belgium. It would have been different if I was from Mexico or Haiti.” Migrants who can fit into the racial or ethnic majority expressed they had an easier path to finding a sense of belonging in the US because their race and ethnicity did not single them out as being different. We embarked on this study to understand the ways in which migrants across legal migration statuses find belonging in a similar receiving context. As important as legal status can be for belonging, we found surprising pathways beyond legal migration status through which interviewees derived (not) belonging. Our sample is composed of a diverse group of migrants: they vary in age, gender, birth country, religion, and length of stay in the US. They also vary in their legal status: some are students or workers, some are permanent residents or citizens, some are refugees, and some have no legal status. Despite the well-documented role of legal status shaping migrant belonging, our sample showed great variety in belonging across legal statuses see . While wTheoretically, our analysis makes two important contributions to the literature on migrant belonging. First, we include migrants with a variety of legal immigration statuses in our analysis. Migrants receive very different levels of official legal, economic, cultural and social support—support that could directly contribute to opportunities for and sense of belonging—depending on their migrant status category when they arrive to the United States. Yet we find that migrants across all four migration categories find (and do not find) belonging, including refugees  and undocumented immigrants . We find that, while permanent legal migration status does serve as a pathway for belonging for some migrants with those statuses, it does not guarantee belonging. Similarly, while lack of permanent legal migration status is a barrier to belonging for some migrants, many find pathways to belonging without permanent status. Our inclusion of migrants with multiple and varied migration statuses reveals the nuanced role of legal migration status in contributing to migrants’ opportunities for and sense of belonging.Second, by grounding our analysis in subject-centered definitions of belonging, we have been able to capture unquantifiable contributors to belonging that researcher-centered definitions often overlook. As described by William (permanent status) in this manuscript’s title, interviewees found belonging when “connecting [with] their communit[ies]”—and connecting with their communities happens in many ways and across many different dimensions. Participants’ responses emphasize the importance of local context in shaping immigrant experiences of belonging, perhaps especially within new immigration destinations. As a result, we encourage scholars and policymakers to see migration, integration, and belonging as largely community-based phenomena, even if they are influenced by policy set at the national level. Local- and national-level policies, practices, cultures, and social norms collectively shape opportunities for and barriers to belonging, opening up multiple pathways for migrants to find and experience belonging in their daily lives.community-conditioned belonging, to emphasize the layered pathways to and conditions that support belonging and wellbeing for international migrants. Community-conditioned belonging highlights that community resources, cultural mores, opportunities to build relationships within the community, and local (anti)racism all shape the place-specific quest for belonging, in addition to—or in spite of—legal migration status and other conditions determined at the national level. This also means that community-level conditions that inhibit belonging can overpower permanent legal status and other national-level, “official” markers of belonging. Community-conditioned belonging suggests that communities play a direct role in migrants’ (non)belonging and can actively influence migrants’ opportunities for and experiences of belonging [We offer a new conceptual lens, elonging ,59,78. Celonging ,87.While our interviewees’ descriptions of (not) finding belonging acknowledged the role of social and legal structures as gatekeepers to belonging, their responses also demonstrated how agency, experience, and opportunity also shaped access to pathways of belonging. These findings suggest that localized and individualized efforts to increase migrant belonging can succeed, in addition to ongoing advocacy for needed changes in national-level policies and programs. Consequently, this study has implications for local, state, and federal policies that promote opportunities for migrant belonging. First, migrants should have a wider variety of paths to permanent residency and citizenship. With more flexible (and efficient) paths to legalization, more migrants (including students and workers) will be able to contribute to their host society and achieve meaningful social, economic, and cultural inclusion. Second, work authorization and protection from deportation should be more accessible for temporary and undocumented migrants. This will allow migrants to more fully contribute to the local, state, and federal economy. Third, we find that cultural familiarity and co-ethnic community connections can create one pathway to belonging for migrants. Local and state governments and community-serving organizations may be particularly effective in innovating policies to aid cultural inclusion for migrants. We also need expanded opportunities for language training, outreach and services to underserved communities, and support for organizations that increase cultural understanding. Local and state governments and organizations can also foster social connections between migrant and non-migrant communities. Not only do migrants need cross-cultural relationships to increase belonging, but both migrants and non-migrants likely benefit from the resources and opportunities that come from such networks. Any of these policy suggestions will aid and encourage belonging for migrants from all legal, economic, social, and cultural backgrounds. Despite barriers to inclusion, policy makers and practitioners can continue to encourage and implement such efforts necessary to help migrants achieve belonging.


Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making. Many people want to remain at home as they get older, especially those with disabilities. However, home healthcare services are becoming increasingly sophisticated and intensive. People with disabilities, chronic diseases, and functional impairments need additional services and support to maintain their independence. When it comes to fulfilling the needs and demands of these populations, home health organizations and other service providers are investigating new models of treatment and payment, as well as the optimum use of their workforce. It is important to consider where home healthcare fits into the wider healthcare system in light of these issues and possibilities. Individuals appreciate the benefits of receiving healthcare at home, and well-managed home healthcare can encourage healthy living and well-being .Home healthcare agencies (HHCAs) are a network of treatment delivered to individuals in their residences by professional staff under the supervision of medical physicians. The Medicare rules are perceived as the standard treatment for all interactions between HHCAs, even if an individual is not insured by Medicare . The staThe high quality of services provided by HHCAs is an essential component for patients in order to improve the provided services. In Medicare, star ratings are of significance and are valuable to analyze the performance of quality services regarding the HHCA. It was proposed in July 2015 by CMS and named Quality of Patient Care star ratings (QoPCsr) with a range of 1–5; “1” means an awful experience about service, and “5” means the consumer is delighted about the system [The world of HHCAs varies from clinics and other agencies, whereby nurses are employed. For this instance, home healthcare workers operate independently in the field with support services provided by a head office. The nurse–practitioner working partnership has considerably less physician communication, and the surgeon relies on the nurse to render decisions and communicate observations to a larger extent . This hiAnother characteristic of HHCAs is that physicians deliver services in a special environment for everyone. There could be situational factors that pose costs for patients that the health professional cannot remove . HospitaInfluential features are one the best ways to obtain favorable results from unstructured data. The identification of novel features concisely improves the measuring evaluators in the quantitative study. The study of HHCAs enables us to establish an effective framework by utilizing data mining techniques for exploring the best house healthcare facilities, as there is a need to declare an ML model that provides promising outcomes.Numerous healthcare professionals at home wondered why they have poor star ratings considering comparable facilities and services. The poor satisfaction rate suggests that patients are reduced to home health services, significantly affecting the healthcare provider’s income over time. Home practitioners’ reputation could also be involved in the poor star ratings. Combined with this, it is challenging to continuously examine enormous amounts of data while discovering complex and dynamic characteristics that are likely to occur, but are obscure to humans . The risTherefore, examining Medicare’s star ratings of HHCAs’ data is a need, and the use of artificial intelligence (AI) in gaining insights from these data will help in establishing an effective framework for evaluating the finest home healthcare facilities. Artificial intelligence, known as machine learning, or ML, can be defined as the application of a number of different statistical methods that can be used to produce predictions and decisions based on similarities between what is currently being examined and what is being identified in the past. To fill this gap, we propose an automated ML-based method for gaining insights from the OASIS and Medicaid claim datasets. The utilized approach will yield the influential factors that are highly dependent on the star ratings. A publicly available home healthcare agencies (HHCAs) dataset  is utiliBinary class and multi-class classification are applied by using four renowned ML models built to justify the robust model for HHCAs’ data.Computing variable significance score for each attribute to analyze the contribution of each indicator in predictive analysis.For experimentation, unstructured data are considered, and statistical techniques are applied to uncover the outperforming indicators.Twelve effective attributes are proposed in this research, which can help in finding the best HHCA. We are the first to explore these features from HHCAs’ data.To emphasize the significance of our work, the following are the contributions that this study makes:The rest of the study is structured as follows: The way doctors and health givers think about disease and treatment has significantly evolved to reflect the changes that have taken place in our patients, our healthcare system, and medical science. The complexity level now present in medicine is beyond the capabilities of the human mind . As a reData analysis tools evaluate various data types and run relevant analyses to gain insights from data records. This is essential when it comes to translating raw patient data into useful information, which can be used to support the decision-making in healthcare organizations ,20. DeleVarious researchers have studied the impact of important factors using ML techniques in prior studies. Most of the studies provide the solution for binary classification and regression analysis in the medical domain. Some important research to find the influential parameters using the ML methods are discussed here.The start was to develop and verify a patient registration system for health promotion that allows patients to be classified according to their skilled nursing needs . NursingPrediction analytics and AI in healthcare face a number of obstacles, including data access, standardization, engagement, computing resource requirements, and the implementation of predictive models . In addiMoreover, rating quality was examined and how confidential rates react to the introduction of a five star rating system for nursing homes . AccordiDuring the literature review, it was noted that there was no similar research on the star rating of reviews using Medicare data with the exception of one paper , where tThe methodology for the home healthcare agencies (HHCAs) framework is discussed in this section. In this research, we considered the “Home Health Care Agencies (HHCA)” dataset, which is a directory of all Medicare-approved home care services . It consDataset pre-processing is a data mining method involving transforming raw information into a comprehensible format. Real-life statistics are frequently unreliable, contradictory, and without any patterns or trends, which would certainly include a large number of mistakes . The HHCIdentifying possible outliers is critical, and it is necessary to remove them before performing an analysis . For thiTwo classifications were conducted on the dataset, including multi-class classification  and binaPredictive analysis is a significant part of this research for creating good enough and timely decisions using HHCAs using the five machine learning algorithms. In this case study, the HHCA dataset was considered for predictive analysis using feature selection and predictive analysis. We uncovered the most influential features and then built the predictive model using the vital feature. The Weka tool was considered the most powerful predictive analysis for building ML predictive models.The proposed study utilized ML and DL models to show the effectiveness of the proposed features. SVM is a powerful algorithm due to its kernel techniques such as the linear, polynomial, and RBF kernel. These kernel tricks are used to transform the data from one feature pace to another feature space, where the data are more linearly separable . Along wA deep analysis is conducted in this portion of the research. All the steps and outcomes from the previous methodology section are employed here to gain valuable insights from the HHCA dataset. Briefly, experiments were conducted on the available data by employing statistical techniques to uncover the helpful attributes from the available data. Moreover, a concise approach was performed using machine learning to build sophisticated models to obtain valuable outcomes from those outperformed characteristics.This subsection performs the feature subset selection for HHCAs using the “40” features to find the most influential indicators. To fulfil this need, we considered the various filters of the WEKA tool for feature selection and dimension reduction. The ranker method and greedy search algorithms find the most important feature from the dataset ,40. PrinThe correlation score of each feature was determined with the help of the Ranker method in Weka . The valThe forward selection of greedy stepwise was considered here for the subset evaluation of the variable’s selection. This approach is also helpful in uncovering the most influential variables from the dataset . The ranThis method also shows the correlation score of the features, and the results are given in Principal component analysis (PCA) was beneficial to deal with the dimensionality reduction of the dataset . It is aThe total, “27” dimensions were used here, depending on the threshold values. Here, we set the default value confidence interval up to 95%, which is the most appropriate for the analysis. We also set the threshold value following our own needs.In this case study, “4” feature selection algorithms were applied in the HHCA dataset to find the best features among them and remove the unwanted or uncorrelated features. Each of the algorithms works to remove several features using their threshold values. Now, selecting one feature is important for approaching further. After applying the feature selection algorithms, the datasets were saved for comparing the capabilities of the different techniques. Therefore, after applying four feature evaluation techniques, we obtained four datasets: Correlation Attribute Evaluation Dataset (CAED), Principal Component Analysis Dataset (PCAD), Relief-F Attribute Evaluation Dataset (RAED), and CFS Subset Evaluation Dataset (CSED). We selected Random Forest (RF) as the baseline classifier to check the capabilities. The performances were checked for binary classification and multi-class classification. A broad discussion on binary and multi-class classification is given in the later sections. Here, a clear winner is the CFS subset evaluation dataset, where the feature was reduced from 40 to 12, a massive selection, yet scoring best among the other selection criteria. Relief F feature selection performed rather close, but in terms of reducing the features, CFS decreased to 28 features, whereas Relief F reduced to only 3. In this subsection, four ML models, including the Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), were implemented on the HHCA dataset. Two types of classification models (binary and multi-class) were implemented to build an effective framework for HHCAs. For this purpose, Weka tools were utilized to make the predictive models for binary and multi-class classification problems. Both approaches have their significance due to their pros and cons. The main objective of the predictive analysis is to find the most robust classifier for the HHCAs that gives the best results. The outperforming ML models for binary and multi-class classification problems were assessed using the four evaluation metrics . The experimental details of both techniques are presented below.This part of the research was based on the binary classification data of HHCAs. The data were labeled with only two classes, “Good” and “Bad”. As we employed only two classes of data to build the ML model, this is why the part was declared as a “binary classification” approach. Various ML models were tested and trained using 10-fold cross-validation. We used renowned models to depict high accuracy, precision, recall, and F-1 measure results. This section of the paper is based on the multi-class classification data of HHCAs. The data were labeled with five different classes, which were “Best”, “Good”, “Average”, “Bad”, and “Very Bad”. As we utilized only multiple classes (five different classes) of data for building the ML model, the part was declared as a “multi-class classification” approach. Various ML models were tested and trained using 10-fold cross-validation. We used renowned models to depict the high accuracy, precision, recall, and F-1 measure results. In this case, we evaluated the ROC results for binary and multi-class classification. As we know, the binary class records have the labels “Good” and “Bad”; therefore, the outcome of the classifier is shown in There are five different classes in the multi-class approach, and we evaluated five different ROCs for each class, illustrated in This research proposed a solution for the Medicare industry to uncover the most influential attributes using the star ratings. Two types of ML techniques were implemented, which included binary and multi-class classification. The HHCA dataset was utilized here, which contains 70 features along with the target variable (star rating). The data pre-processing was performed along with handling missing values, removal of inconsistencies, and elimination of outliers from the dataset. Afterward, feature engineering was conducted using Weka, and four different attribute selection filters  were applied to locate the most impactful attributes for binary and multi-class classification problems. The RF model was chosen for these “4” filters to find the best feature selection technique, and the findings depicted that the CSE filter had the best performance for both techniques  using the “12” features. The name of the important factors are O_H_H_A_Ser, h_t_p_c_t_m, r_c_f_s, f_c_t_p_f_c, b_m_a, h_g_o_b, g_ b_a_b, p_b_i, i_h_a_o, a_d_c_b_m, t_b_a_t_h, and P_iS_a. After selecting the best from the 70 available attributes, four renowned machine learning models  were utilized for binary and multi-class classification using the “12” features. The ML model’s performance was analyzed based on four evaluation metrics: accuracy, precision, recall, F-1 score. For better results, the hyperparameters’ tunings were also considered, and we tested the models with the best hyperparameters. The findings showed that the DNN and RF models outperformed and achieved the highest score among all other models for binary and multi-class classification, respectively. The ROC was also a significant evaluation metric for finding the significance of the model. We computed the ROC results for each class in both binary and multi-class classification. The findings of this research are helpful in the healthcare domain to improve the customer experience using the influential features for better results.The twelve significant extracted features affecting Medicare’s star rating had the following features: Offers Home Health Aide Services, how often the home health team began their patients’ care in a timely manner, how often the home health team determined whether patients received a flu shot for the current flu season, How frequently did the home health care staff follow the doctor’s recommendations, provide foot care, and provide education to people with diabetes, Frequency with which patients improved in their capacity to walk and move, Rate with which patients improved their ability to get out and back into bed, How frequently patients improved their ability to shower, Note on the frequency with which patients’ breath improves, Rate at which surgical incisions healed or improved, Rates at which patients improved their oral medication adherence, Frequency with which home health care recipients were hospital admission, PPR Risk-Standardized Rate (Upper Limit).Initiation of Care at the Appropriate Time was one of the important features, h_t_p_c_t_m, that was yielded in this study, and it affects the Medicare star rating. It shows the percentage of home health quality occurrences when treatment began or resumed. Timely Initiation of Care  came firAppropriate timing is one of the important aspects preventing costly rehospitalizations and improving patient outcomes. When home healthcare was delayed after hospital discharge, the patients were more likely to experience a 30-day rehospitalization, and the association between it and the rehospitalization of diabetes patients was investigated in , while iImprovement in bathing, g_b_a_b, was another feature that was yielded in this investigation that affects the star rating. The percentage of home health quality episodes in which the patient improved his/her ability to bathe himself/herself on his/her own is reported by the “Improvement in Bathing ” featureAnother aspect of this study’s findings that impacts the star rating was the “Improvement in the status of surgical wounds”, i_h_a_o. Home care wound management was tested in  to see icms.gov accessed on: 16 March 2022). In the dataset, a few columns are text-based, which we did not consider for this research. These could be considered for future analysis. Moreover, for future studies, some of the columns were removed because of the many missing values. These missing values can be handled by employing regression techniques for building the solution . Thus, significant features can be discovered by considering the neglected parameters. Modern machine learning models are very sophisticated and favorable to adopt by the agencies. Thus, deep learning and ensemble models can be helpful to optimize the performance of models to obtain the optimal results.The future work of this research is a feasible guideline for researchers. The data specialist can bring valuable insights from the available data residing on the website (


It is forecasted that the skills and competencies necessary for post-pandemic success in higher education need to be founded upon adaptability, coping, and Self- Regulated Learning (SRL). It is worth investigating how stakeholders perceived their adaptability and coping with the accelerated change accompanying Coronavirus Disease of 2019 (COVID-19). Accordingly, the purpose of this study was to assess the self-reported adaptability of postgraduate dental learners and their instructors in the context of abrupt transition to distance learning induced by the pandemic. This study utilized a convergent mixed methods study design. The quantitative and qualitative data were concurrently collected from instructors and learners, using a tailor-made survey of items measured with a Likert-type scale, complemented with open-ended questions. The collected datasets were independently analyzed. Descriptive and inferential analysis were conducted using the quantitative data. Qualitatively, the researchers underwent a multi-staged thematic analysis. The generated information was then integrated using a joint model analysis. The percentage of the total average of self-reported adaptability for both groups of stakeholders was 81.15%. The instructors, with a mean of satisfaction of 17.94 (±1.76), rated their adaptability significantly higher than the learners, with a mean of satisfaction of 15.66 (±2.77) (p = 0.002). The thematic analysis resulted in two interrelated themes: Self and Environment. Within the Self theme, three subthemes surfaced: Cognitions, Emotions, and Behaviors. As for the Environment theme, it encapsulated two subthemes: Enablers and Impediments. The stakeholders perceived themselves to have adapted well to the transition, and SRL appeared as a cornerstone in the adaptation to the accelerated change accompanying COVID-19. There appeared to be an interplay between the cognitions, emotions, and behaviors on the level of the self as part of the adaptation process. Also, building upon existent models of SRL, this study uncovered that the stakeholders considered the environment to play a crucial role in their adaptation process. This highlights the importance of developing a climate that remains, despite external pressures, conducive to attaining learning and teaching goals. It is also crucial for university-level mental health promotion activities to proactively foster, among learners and instructors, adaptability, building ‘academic resilience’. The COVID-19 pandemic made characterizing today’s world as Volatile, Uncertain, Complex, and Ambiguous (VUCA) more relevant than ever before . This paThis accelerated change was evident in higher education , where lThis situation revealed how inadequately prepared higher education was for radical transformation –16. ThisIt is forecasted that the skills and competencies necessary for post-pandemic success in education during the next five years need to be founded upon adopting skills related to emotional intelligence, learning and innovation, and information, media, and technology –22. AccoIt is worth investigating how well the relevant stakeholders perceived themselves to adapt and cope with the accelerated change (at the onset of the pandemic), and with the corresponding VUCA, accompanying COVID-19. Several tools exist to measure coping including the Coping Strategy Questionnaire (CSQ)  and CopiThe importance of measuring how adaptable the stakeholders perceived themselves to be lies in the extensively studied theories of behavioural change. There appears to be an established understanding that how efficacious human-beings perceive themselves to be  affects the extent to which they exhibit efforts, and their behaviours, and ultimately their performance , 28. AccHow adaptable did the stakeholders under investigation perceive themselves to be?How did the stakeholders adapt their approaches  to cope with the accelerated change?How can other postgraduate dental schools proactively raise the level of adaptability of individuals and the all-encapsulating institutions?This study took place at the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, UAE in the Hamdan Bin Mohammed College of Dental Medicine (HBMCDM). This postgraduate dental college offers three-year full-time specialty dental postgraduate programs in prosthodontics, periodontics, pediatric dentistry, orthodontics, and endodontics.nd March 2020 until the end of the respective academic year. Despite all the surfacing impediments, HBMCDM stakeholders managed to continue all didactic educational activities as previously scheduled , which determines where the four corresponding values lie on the predefined scale. Finally, an overall score of adaptability  was calculated. For which, again, the percentage of the total of the means was calculated [dividing the respective total of the means by 20  and multiplying it by 100], which determines where the corresponding value lies on the predefined scale.Since the scale used for capturing the perception of the learners and instructors was tailor-made for this study, the validity tests of Cronbach’s Alpha, and the Principal Component Analysis (PCA) of the Kaiser-Meyer-Olkin (KMO) and Bartlett’s test were performed to ensure internal consistency and check external variance, respectively, of the adapted tool.To select the appropriate inferential analysis test, a test of normality was conducted for each of the four components and for the overall score of adaptability. The data of each of the four components, independently, and the overall score of adaptability all turned out to be not normally distributed. Accordingly, the Mann-Whitney test was used to compare the overall score of adaptability, and each component independently, between both groups of stakeholders (learners and instructors).The qualitative data analysis started after the completion of the data collection phase. The data was inductively analyzed using thematic analysis. The process of analysis followed Braun and Clarke (2006)  six-stepNVivo software version 12 plus  was used to code the data, and in turn, facilitate the categorization of the relevant text fragments. The data collected from each of the two groups of stakeholders was handled separately.First, two researchers (FO and FAR) familiarized themselves with the data by examining and re-examining the qualitative data. Second, the qualitative, narrative data was examined line-by-line and initial codes were generated. The two researchers underwent several rounds of discussions to resolve any discordances between their observations. Third, the initial codes were inductively investigated to be combined into subthemes, which in turn went through a similar iterative process to construct themes. The generated themes and sub-themes were then reviewed as part of stage four to ensure that the data within each compartment  are sufficiently common and coherent, also the compartments are adequately distinct from each other. As part of stage five, themes and subthemes were coded and defined in the context of the study. The last step constituted of reporting upon the findings of the qualitative analysis, which was done in alignment with recently published standards of reporting on qualitative data analysis integral to mixed methods research design .After completing the independent quantitative and qualitative analyses, the outputs were systematically integrated via joint display analysis. This involved merging the results from the two datasets through a side-by-side comparison to assess the best way to map the findings onto each other. This iterative process enabled developing a whole that is greater than the sum of its parts , 35. As The Pillar Integration Process (PIP) framework, initially presented by Johnson et al. (2017) , was selThe final respondents’ number was 53 out of 63 learners . As for the instructors, 18 faculty members out of 21 responded . A unique identification number was given to each of the 71 participants. It was complimented with ‘R’ for the learners and ‘I’ for the instructors .The reliability score of Cronbach’s Alpha for the evaluation instrument, which captured the stakeholders’ perception, was 82.4%. The percentage of the total average of the learners, instructors, and both groups of stakeholders were 78.3%, 89.7%, and 81.15%, respectively, as per With a KMO close to 1, the sampling was determined as adequate. Also, according to the Bartlett’s Test of sphericity, the null hypothesis got rejected with an identity matrix in which all the diagonal elements were 1 and all off-diagonal elements are 0. As such, the PCA  showed that 87.1% of the variance can be explained by the instrument, as a whole (p < 0.001). This means the instrument is both reliable and valid to measure what it is intended to measure.As illustrated in The thematic analysis resulted in two interrelated themes: Self and Environment, as illustrated in this study’s conceptual framework . Within This theme focuses on intrapersonal factors that appeared to influence the learners’ and/ or the instructors’ adaptability.Cognitions. This subtheme refers to thought processes that the individual learners and instructors seemed to be experiencing. These thoughts appeared to be related to oneself, other people, and/ or the context of experience.Oneself (including meta-cognitions). The stakeholders appeared to be judging themselves. They have somehow developed an opinion about how well they adapted to the situation.L-9: “…I adapted and performed much better than I expected…”I-19: “…I believed in myself; I was confident about my abilities to successfully transition to distance learning….”In many instances, the learners and instructors appeared to be interpreting their own thoughts and understandings during the transition to distance learning.L-10: “…I could not stay focused for long periods of time…”L-31: “…most of the time, I was not able to focus my thoughts. I frequently experienced brain fogs….”I-11: “…I was quite confused, at the beginning. For example, I was not sure if I needed to look at the laptop camera or at the screen where my presentation was shared from my desktop…”Other people. The study participants reflected upon their own thoughts and opinions about others.L-5: “…not sure if other students were paying attention during the lectures…”I-11: “…at the beginning, I was doubting the students’ engagement…”I-12: “…I think we became more interactive, over time. At the beginning, it was a challenge to both groups of stakeholders: the instructors and students. Eventually, we comprehended what a virtual classroom is…”Context of experience. The participants also discussed their views around aspects related to their environment and the context of the experience. Some learners perceived the distance learning experiences to be difficult:L-16: “…I found the experience to be quite challenging; I could not concentrate at home. It is not the right place to attend a class from…”L-46: “… distance learning is sort of different from that which happens on campus in terms of motivation and interactions…”I-11: “…the idea of finding myself on my own in my room, interacting with a screen was quite challenging…”Others showed openness to and acceptance of the new experiences. They were grateful to the fact that the virtual environment enabled the continuation of learning in a time when in-person activities became unsafe.L-9: “…it has been a pleasant new experience… I was intentional about adapting to the reality of the situation. I perceived it to be to our own benefit…”L-45: “…at the beginning I was wondering how it will be. Later, I got surprised by how smooth the transition was…”I-7: “…the distance learning was the best available alternative…”I-9: “…the online teaching experience bought with it plenty of new opportunities. It has broadened the scope of learning and teaching…”Emotions. This theme sheds light on the emotions that the participating learners and instructors experienced in adjusting to the distance learning and teaching. Some emotions were positive, and others were negative.Positive . The study participants expressed several positive emotions:L-7: “…I felt excited about trying this new experience…”I-19: “…I am happy about all facets of distance learning…”Negative. The stakeholders also expressed negative emotions. Its sudden onset and all the uncertainty around it were quite disturbing to some learners and instructors.L-17: “…I was doubtful…”I-6: “…I felt slight apprehension…”I-8: “…I felt skeptical about the feasibility of the transition and maintenance of distance learning. How are we supposed to acquire the needed skills virtually? We are meant to become clinicians after all…”Several learners referred to an elevation in their level of anxiety. Besides worrying, the learners also expressed anger, frustration, and changes in energy levels. Between caretaking, housework, kids’ homeschooling, and learning, some found personal and professional time blended.L-2: “…I experienced feelings of frustration and exhaustion- there were many competing responsibilities, all taking place at home…”L-3: “…I felt confused. Also, I got frustrated due to the many internet-related problems that I faced. This led to time management issues…”L-46: “…I felt anxious, but I had trust that MBRU will find a way around the challenge, and will continue on providing us with the best…”Behaviors. This theme encapsulates all the different actions that the stakeholders partook to adapt to the change. It also included all the skills needed to exercise to keep up with the challenges inherent in the transition. The stakeholders differentiated between the behavioral changes that turned out to be conducive and those that appeared to them not to add any value to their experiences.Constructive. Among the actions that were deployed, some turned out to be to the advantage of the learners and instructors. Some learners proactively developed opportunities to connect with peers; their engagement in virtual study groups added value to their learning strategies during the transition to distance learning.L-14: “…I developed the habit to regularly meet with my colleagues online to study the lectures together…”L-15: “…participating in group study was one of the best ways to keep distance learning as similar as possible to that of typical conditions….”The learners also developed and deployed their time management skills.I-7: “…I had to adjust my schedule to make better use of my time…”Also, some learners were intentionally more engaged in the virtual context, relative to face-to-face interactions.L-47: “…I started reading and researching more. I also developed the habit to prepare for the lectures, before the actual time the classes take place…”L-7: “…I focused on preparing for the lecture ahead of time, and I maximized my participation during the meeting. It was clear that our instructors were trying their best to make-up for the interpersonal gap. When I have a lecture to present, I try to have to have pauses every now and then with a funny slide or so to refresh the energy of my colleagues…”Instructors adjusted their teaching strategies to engage learners. They adopted techniques to foster meaningfulness in their connection with the learners and overcome physical and emotional isolation.I-11: “…I started to share more links, videos, and reading materials with my residents to further support their learning. We arranged for online meetings on Teams to discuss their research projects, and address and reflect upon inquiries related to their presentation…”I-12: “…our residents stayed in touch via WhatsApp groups that were created during the lockdown. It made connecting with and updating each other easier…”Some stakeholders realized that they were proactive in modifying their physical environment at home for it to become more conducive to their learning and teaching targets.L-22: “…I arrange a studying set-up at home… I made the effort to change this set-up from time to time…”I-11: “…allocating a working space at home was also very helpful. I get into my work mode as soon as I land on this desk…”Futile. In their attempt to cope with the transition, some stakeholders resorted to behaviors that did not add value to their experience. Learners were suddenly faced with many responsibilities that they needed to attend to concurrently from the same space. A few of those learners seemed to deal with all their responsibilities as one chunk, without any sort of compartmentalization.L-12: “…It has been difficult to suddenly be required to manage both family and university at once, in the same place…”Other learners could not bear the fear and uncertainty inherent in the situation and ended up overworking themselves as a coping mechanism.L-7: “…the transition and isolation, and all the accompanying stress and fear are making me spend most of the time studying, which is getting quite stressful. I barely have any time left for myself. What I used to finish in one hour, now takes me 3 hours…”Some learners needed to lessen their level of interactions to deal with their anxiety and insecurities.L-24: “…I was hesitant to participate in the class unlike in the normal class setting I would have been more interactive…”This theme refers to external factors that the stakeholders perceived to have affected the learning and teaching experience positively or negatively.Enablers. Among those external variables that the stakeholders shed light on were ones that inspired, enabled, and/ or empowered the learners and their instructors. For example, the stakeholders particularly emphasized that the understanding of family members was crucial for effectively transition.L-21: “…the most important thing that my family understand the situation and do not interrupt me during my classes…”Also, relying on family and friends for support and comfort was frequently brought-up by the study participants. The role of close family members appeared to be quite helpful in facilitating the individual-level adjustments that needed to take place.L-47: “…it was not very difficult to cope on my own; my family was supporting and comforting me all the time…”I-9: “…I coped well; thanks to good friends and family…”In some cases, the instructors shed light on how the fact that they had rapport with the students enabled and smoothened the transition.I-11: “…I think it is more convenient to move to distance learning after knowing the residents and they get to know you through face-to-face interaction…”The participating stakeholders highlighted that one of the significant external resources was the educational institution itself. This included how the institution led the transition and provided all different kinds of support at various institutional levels to both learners and instructors.L-24: “…the university was very cooperative…the instructors made me feel at ease, my colleagues kept on sharing with me stories of the obstacles that they had to go through and how they overcame them….”I-12: “…the transition has been managed professionally by MBRU leaders and staff members, along with faculty members and other stakeholders who are directly involved in the learning and teaching. I perceive the transition to have happened smoothly…”I-15: “…transition was a lot easier than I expected; thanks to Information Technology team support…”Impediments. The stakeholders also identified external variables that they felt held them back, discouraged them, and/ or slowed them down. For example, learners indicated the challenge of the competing responsibilities that arose because of the pandemic and that needed to be attended to concurrently from the same physical space. They needed to strike a balance between keeping up with their educational duties and their personal and/ or familial life.L-2: “… personally, I have no free time to read any supplementary material assigned by my instructors. I am always busy; we have to be available 24 hours a day, 7 days a week for anything or everything at home…”L-3: “…it was quite challenging in the beginning. I could not continue having a part-time homecare nurse to support me with taking care of my father. Her part-time constituted a risk in terms of transferring the virus in between her multiple work duties…”L-38: “…All of a sudden, I needed to keep an eye on my kids of 5 and 3 years of age, along with homeschooling them, while living-up to my learning obligations…”L-25: “…I disliked it! I am a mother and having my kids around does not make it easy to focus. My kids need home schooling and supervision while I am having my classes…”Some stakeholders considered that the lack of opportunities of hands-on and clinical experiences constitute a hindrance or obstruction towards their learning or teaching objectives.I-2: “…in some areas it was ok, but others require interaction with students and hands-on experience…”L-5: “…I miss my clinical work and patients, which is demotivating me…”The sudden transition to the online environment accompanied by the isolation due to the social distancing directives constituted to almost all participants an external challenge that they needed to overcome. Some of the learners mentioned the loss of connection with others due to isolation. There were also the inevitable adverse effects of using electronic devices for long periods, which caused digital eye strain and/ or headaches among the learners.L-14: “…it was hard to study at home sometimes, due to isolation. Studying on my own without my colleagues really affected me…”L-14: “…I noticed that I was regularly experiencing headaches while studying from home, which was not the case prior COVID-19. These episodes were maybe induced by the heavy reliance on technologies and electronics…”Disruption of daily routine and its consequences were repeatedly referred to by the learners and instructors. The switch to digital learning also resulted in sleep deprivation among our stakeholders. This, in turn, generated fatigue and in some cases burnout; the stakeholders observed that they were stretching themselves too thin.L-2: “…the changes to my routine and increasing responsibilities were accompanied with lack of sleep. My lifestyle has not been healthy and there is very little that I can do about it…”L-7: “…my personal space got conquered; I do not have the time now to recharge my own energy…”I-5: “…not having a real break. It can get really tiring. With the lectures and meetings, I feel like I have been working 24/7…”The PIP joint display visually conveys the inferences of the quantitative and qualitative analyses and meta-inferences generated by merging the outputs of both analyses. As depicted in This study reinforced the notion that students are self-regulated agents who can manage their learning . It alsoThis study demonstrated SRL as a cornerstone in the adaptation to the accelerated change (accompanying COVID-19) in health professions’ education of the individual learners and their instructors. Both groups of postgraduate dental education stakeholders (the learners and instructors) experienced and reported variation in their cognitions, emotions, and behaviors. There was an evident interplay between these individual-level variables, which enabled the stakeholders to adapt to the abrupt change. The stakeholders in the current study also highlighted attributes of the environment that they believe have played a role in their adaptation processes. Along those lines, a recently conducted cross-sectional study aimed at investigating the associations between adaptability, personality, and levels of learning  among university students, revealed that adaptability to the pandemic was associated broadly with more positive reactions across multiple indicators . This stThe inclusion of instructors in the SRL that takes place in the process of adapting the learning and teaching to accelerated change is not unique to this study. It was previously suggested that the experiential learning that health professions students go through usually entails “strong emotions”. A key responsibility of facilitators of such learning experiences is to harness an environment of trust, authenticity, mutual respect, and integrity . This, aThe evidence-driven conceptual framework generated from this study confirms the commonality across existent models of SRL and builds upon them . These mThis study shows that the tailor-made quantitative tool is internally reliable and externally valid in the context of this study. There are several tools published in the literature that are designed to measure self-reported adaptation and/ or coping with change including the Coping Strategy Questionnaire (CSQ) , Coping These tools proved of great usefulness across several contexts yet are considerably thorough and time-consuming. There are relevant tools that are more succinct in measuring resilience such as the Brief Resilient Coping Scale (BRCS)  and the It is worth noting in terms of the participants’ self-awareness, the modifications in behaviors were the most obvious and easiest to identify in this study. Along those lines, in another exploratory study during COVID-19 , modificThe current study is characterized by a few caveats. It relied mainly on self-reported data. Each of the two groups of stakeholders provided some reflective data on the other party’s adaptability. It would be interesting for follow-up studies to further explore this point-of-view by systematically enabling observer rating. This will allow for comparing how the perception of one’s adaptability maps onto how others perceive one’s adaptability. Also, the study relied on a small sample of participants, all of whom were from the same institution. This, however, is not uncommon in research studies which rely on mixed methods design since the actual value is on developing a thorough understanding of the subject matter. As such, the emphasis becomes on the transferability of the findings as opposed to their generalizability. It would be interesting for investigate the self-reported adaptability of university stakeholders, across several higher education institutions, in times of abrupt change. This will enable the generation of findings that are more generalizable that the ones which surfaced from the current study. Moreover, in alignment with the principles of the Institutional Research function at MBRU, complete anonymity of the participating university stakeholders was maintained. Therefore, demographic details of the participants  or that relating to their affiliation with the university  were not recorded. It would have been interesting to investigate the association between the stakeholders’ extent of adaptability and those independent variables. Also, although this study offered a lot of insight into how the stakeholders perceive themselves and each other when it comes to adaptability, its cross-sectional design did not enable investigating and in turn establishing causality. Hence, it will be great for future studies to be based on longitudinal designs, where potential antecedents to adaptability are captured. The findings of such studies will have substantial practical implications where higher education decision-makers will get a better grasp as to what variables they can foster to proactively raise the level of adaptability among their stakeholders. The study offered a lot of value through the open-ended questions integral to the survey in terms of exploration. Yet, we believe it is worth deploying alternative data collection tools  to develop an even more thorough understanding of the adaptability experiences of the stakeholders.This study emphasizes the importance of viewing learners as self-regulated agents that are actively adapting to their external environment throughout their learning journey, especially during extreme conditions such as that faced at the onset of COVID-19. This key observation is expected to encourage key opinion leaders in higher education institutions to leverage SRL theories to proactively inspire and empower the learners and their instructors. It also reveals the importance of developing and maintaining safe and nurturing learning environments that foster connection and mattering (to one another), enable cognitive flexibility, and build academic resilience.S1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.