A total of 232 out of 244 medical students completed both the baseline and follow-up questionnaire-based surveys, resulting in a response rate of 95{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}. Demographic data was comparable between the two cohorts (Table 1).
Table 1 Baseline comparison of the characteristics of participants belonging to the COV-19 and postCOV- 19 cohorts
Improvement in self-confidence for unit 1
First, it was evaluated whether the respective teaching methods in both cohorts resulted in an improvement in the self-confidence of students regarding their surgical skills. While analyzing unit 1 (sterile working), we found that both the COV-19 (Fig. 2A) and postCOV-19 (Fig. 2B) cohorts showed significant improvement in post-course confidence compared to pre-course confidence. This result was observed for all five subcategories of unit 1 (Table 2).
Fig. 2
Self-assessment comparing pre- and post-course confidence of COV-19 and postCOV-19. Spider web graphs displaying the difference between pre- (full line) and post- (dotted line) course self-assessment. Unit 1 (sterile working): A (COV-19) + B (postCOV-19); unit 2 (knot tying and skin suturing): C (COV-19) + D (postCOV-19); unit 3 (history and physical): E (COV-19) + F (postCOV-19). COV-19 = cohort of summer semester 2021 (full COVID-19 restrictions), postCOV-19 = cohort of winter semester 2021/2022 (reduced COVID-19 restrictions)
Table 2 Self-assessment of pre- and post-course confidence of unit 1
Improvement in self-confidence for unit 2
While analyzing unit 2 (knot tying and skin suturing), we observed that both the COV-19 (Fig. 2C) and postCOV-19 (Fig. 2D) cohorts exhibited significant improvement in post-course confidence compared to pre-course confidence. This result was similar for all five subcategories of unit 2 (Table 3).
Table 3 Self-assessment of pre- and post-course confidence of unit 2
Improvement in self-confidence for unit 3
Upon analyzing unit 3 (history and physical), we identified that both, the COV-19 (Fig. 2E) and postCOV-19 (Fig. 2F) cohorts, revealed significant improvement in post-course confidence compared to pre-course confidence. This result was observed for all three subcategories of unit 3 (Table 4).
Table 4 Self-assessment of pre- and post-course confidence of unit 3
Having established that both the traditional interactive face-to-face hands-on courses and the newly developed interactive remote learning courses were able to significantly improve the confidence of medical students regarding basic surgical skills, it was necessary to determine the course that resulted in a higher difference between the pre- and post-course confidence and the subgroup of students that would benefit the most from a particular teaching method. Subgroup analysis was performed based on sex (male/female), age group (19–22 years/23–29 years/≥30 years), and prior surgical experience (with and without prior surgical experience) for evaluating the difference between the pre- and post-course self-assessment (Δ self-assessment).
Subgroup analysis
Sex
The cohorts were first stratified based on the sex (male or female) of the participants, and the subgroup that benefited the most from a particular learning method was determined. For unit 1, the mean Δ self-assessment in the COV-19 cohort was significantly higher in male students (1.96) than in female students (1.44) (p = 0.0003). However, in the postCOV-19 cohort, the mean Δ self-assessment was significantly higher in female students (1.57) compared to male students (1.29) (p = 0.0372) (Fig. 3A).
Fig. 3
Subgroup analysis comparing pre- and post-course self-assessment (Δ self-assessment). A subgroup (sex: male vs. female) analysis for differences in Δ self-assessment, B) subgroup (age: 19–22 years vs. 23–29 years vs. ≥ 30 years) analysis for differences in Δ self-assessment, C) subgroup (prior surgical experience: with vs. without surgical experience) analysis for differences in Δ self-assessment, D) analysis for differences in Δ self-assessment comparing COV-19 vs. postCOV-19. Data are presented as mean and compared using Student’s t-test or ANOVA. A p-value less than 0.05 was considered statistically significant. Significance is indicated by the following symbols: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.00001, ns = not significant. COV-19 = cohort of summer semester 2021 (full COVID-19 restrictions), postCOV-19 = cohort of winter semester 2021/2022 (reduced COVID-19 restrictions)
For unit 2, the mean Δ self-assessment in the COV-19 cohort was significantly higher in male students (2.59) compared to female students (2.16) (p < 0.0001), whereas no significant difference between males (1.92) and females (2.01) was observed in the mean Δ self-assessment in the postCOV-19 cohort (p = 0.0813) (Fig. 3A).
Nonetheless, for unit 3, we found that the mean Δ self-assessment was comparable between the female and male groups in both cohorts (Fig. 3A).
Age
The two cohorts were stratified based on age, which resulted in three subgroups: 19–22, 23–29, and ≥ 30 years. For unit 1, we found that the mean Δ self-assessment in the COV-19 cohort was the highest for the participants in the age group of 23–29 years (mean Δ self-assessment = 19–22 years: 1.51; 23–29 years: 1.82; ≥30 years: 1.42). Furthermore, the mean Δ self-assessment was significantly higher in students of ages 23–29 years compared to those in the age group of 19–22 years (p = 0.0234). However, no significant differences in the mean Δ self-assessment were observed between the subgroups 19–22 years and ≥ 30 years (p = 0.8443), as well as the subgroups 23–29 years and ≥ 30 years (p = 0.0761).
By contrast, the mean Δ self-assessment of unit 1 did not vary significantly between different age groups in the postCOV-19 (mean Δ self-assessment = 19–22 years: 1.58; 23–29 years: 1.33; ≥30 years: 1.23) cohort (Fig. 3B).
Considering unit 2, we determined that the youngest (19–22 years) subgroup exhibited the maximum improvement in self-assessment for the COV-19 and post-COV19 cohorts. In the COV-19 cohort, the mean Δ self-assessment was significantly higher in the subgroup with participants aged 19–22 years compared to the subgroup with participants aged 23–29 years (p = 0.0017). However, there was no significant difference between the subgroups with participants aged 19–22 years and ≥ 30 years (p = 0.4096), as well as the subgroups with participants aged 23–29 years and ≥ 30 years (p = 0.5073).
In the postCOV-19 cohort, the mean Δ self-assessment was significantly higher in the subgroup with participants aged 19–22 years compared to the subgroups with participants aged 23–29 years (p = 0.0020) and ≥ 30 years (p = 0.0017). In contrast, there was no significant difference observed between the mean Δ self-assessment of the subgroups with participants aged 23–29 years and ≥ 30 years (p = 0.2499) (Fig. 3B).
Upon analyzing unit 3, the mean Δ self-assessment in the COV-19 cohort was significantly higher in the youngest students (19–22 years) compared to the subgroup with participants aged 23–29 years (p = 0.0061) in COV-19. However, there was no significant difference in the mean Δ self-assessment between the participants aged 19–22 years and ≥ 30 years (p = 0.0934) and 23–29 years and ≥ 30 years (p = 0.9923).
Nonetheless, for unit 3, the mean Δ self-assessment was significantly higher in the subgroup with participants aged ≥30 years compared to subgroups with participants aged 19–22 years (p = 0.0224) and 23–29 years (p = 0.0181) in the postCOV-19 cohort (mean Δ self-assessment = 19–22 years: 1.73; 23–29 years: 1.68; ≥30 years: 2.35). However, no significant difference was noted in the mean Δ self-assessment of subgroups with students aged 19–22 years and 23–29 years (p = 0.9332) in the postCOV-19 cohort (Fig. 3B).
Prior surgical experience
Lastly, the two cohorts were stratified based on prior surgical experience. Students without prior surgical experience showed a significantly higher improvement in their self-assessment of post-course confidence compared to pre-course confidence. This result was found for unit 1 and 2 in the COV-19 (unit 1 = mean Δ self-assessment with surgical experience: 0.58; without surgical experience: 1.74; p < 0.0001; unit 2 = mean Δ self-assessment with surgical experience: 1.65; without surgical experience: 2.14; p < 0.0001) and postCOV-19 cohorts (unit 1 = mean Δ self-assessment with surgical experience: 0.77; without surgical experience: 1.57; p < 0.0001; unit 2 = mean Δ self-assessment with surgical experience: 1.15; without surgical experience: 2.10; p < 0.0001).
However, for unit 3, we observed that the mean Δ self-assessment did not vary significantly between students with and without prior surgical experience in the COV-19 cohort (mean Δ self-assessment with surgical experience: 1.21; without surgical experience: 1.09; p = 0.2242) but was significantly higher for students without surgical experience in the postCOV-19 cohort (mean Δ self-assessment with surgical experience: 1.19; without surgical experience: 1.89; p < 0.0001) (Fig. 3C).
To summarize, the mean Δ self-assessment was the highest in the young (19–22 years) male students without surgical experience in the COV-19 cohort and young (19–22 years) and elderly (≥30 years) female students without surgical experience in the postCOV-19 cohort.
Finally, we compared the mean Δ self-assessment of both cohorts using each unit. Both, the COV-19 (Δ self-assessment: 1.58) and postCOV-19 (Δ self-assessment: 1.46) cohorts showed comparable (p = 0.1485) results for unit 1. For unit 2, the mean Δ self-assessment was significantly (p < 0.0001) higher in the COV-19 cohort (Δ self-assessment: 2.26) compared to the postCOV-19 (Δ self-assessment: 1.98). In contrast, for unit 3, the Δ self-assessment was significantly (p < 0.0001) higher in the postCOV-19 cohort (Δ self-assessment: 1.76) compared to the COV-19 cohort (Δ self-assessment: 1.1) (Fig. 3D).
A total of 179 out of the possible 663 students (27{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} completion) completed the online survey in June 2020. Median age of students was 19 years (IQR 18–28 years) and there were approximately three times as many females as males (Table 1), reflective of the undergraduate health sciences cohort (70{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} female). Student numbers were also reflective of the broader enrolment numbers in the programs (i.e., occupational therapy is the largest program). Just over half (53{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}; n = 94) of students had no prior experience in undertaking a Bachelor degree, and 76{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students had not completed any online courses prior to enrolment.
Table 1 Demographic characteristics
Quantitative results to the sense of belonging questionnaire
In terms of students’ sense of belonging to the university, the majority felt ‘quite’ or ‘extremely’ happy with their choice of university (74{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) and felt ‘quite’ or ‘extremely’ welcomed by the university (68{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}). While most students felt respected by both staff (70{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) and students (60{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) at the university, students reported less connectiveness (23.5{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf}) to the university. Only 20{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students reported they felt they were understood as an individual, and only 13{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} felt they ‘quite’ or ‘extremely’ mattered to others at the university (Table 2).
Table 2 Online learning and Sense of Belonging to the University [1]
Table 3 shows how the online learning experiences impacted on students’ perception of the course; 27{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students felt ‘quite’ or ‘extremely’ connected to staff while 16{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students felt ‘quite’ or ‘extremely’ connected to other students. While 49{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of students rated 4 and above for the level of respect that they received from other students and their contribution towards the subject, students who had prior higher education felt less respected than students who had no prior higher education (p = 0.03). When asked how the online subject had contributed to understanding, knowledge/skills in their chosen health profession, about half of the students rated the online subject highly (rating 4 and above). Students who had prior higher education indicated higher ratings of understanding and knowledge/skills compared to students without prior higher education (p = 0.07 and p = 0.03 respectively). There was also a significantly higher proportion of students with no prior higher education who identified the online learning experience as either ‘quite’ or ‘extremely’ likely to impact their intention to continue with their current course (p = 0.001).
Table 3 Impact of online profession-specific subject on perception of the course
Qualitative results
Qualitative findings provided insight into experiences of staff and students during the rapid, unplanned transition to online learning. Student questionnaire responses included two open-ended questions expanding on enablers and barriers to sense of belonging. These yielded 145 enablers and 254 barriers to students’ feeling a sense of belonging. Data were subjected to qualitative content analysis by two authors and categories are presented in Additional file 1.
Three focus groups were conducted: two student sessions, each with two students enrolled in Speech Pathology and Paramedicine, and one academic session with five participants. Four full time academics and one casual academic participated from a total population of nine eligible academics. Using the processes described in the methods, focus group analysis was compared with the survey content analysis and the authors identified synergies between them. Findings were then integrated under a global theme, underpinned by organising and basic themes. The following themes reflect triangulation between academic and student focus group data in addition to survey responses.
Global theme—navigating belonging during the COVID-19 crisis: a shared responsibility
“We are in this together…making the best of this”
This theme explores sense of belonging creation during this period as a shared process, where participants perceived they worked together to get through the crisis. Students and academics encountered many challenges as they transitioned to online learning but despite hard times, were able to engage positively. The global theme revealed students and academics were navigating belonging during the COVID-19 crisis, and this journey was a shared responsibility. Both groups were working to achieve positive student engagement that would in turn create a sense of belonging in first-year students. A strong commitment of working hard to make the best out of this was mutually acknowledged.
Students perceived academics had done “a really good job at making sure we belonged…in those first few weeks that we were on campus but even more so probably while we were in Zoom” (Student-Astrid-Focus Group). Academics perceived students were actively engaged in making online learning work and were collegial and collaborative.
The shared experiences about navigating belonging during the COVID-19 crisis, have been captured under four organising themes: dimensions of belonging, individual experiences and challenges, reconceptualising teaching and learning, and relationships are central to belonging. Within each organising theme, basic themes were identified that provide depth to the organising theme (Fig. 1). Additional files 1 and 2 present a summary of the quotes obtained from the open-ended surveys and focus groups respectively, that contribute to the themes in Fig. 1.
Fig. 1
Pictorial representation of the global, organising, and basic themes
Organising theme: dimensions of belonging
This theme outlines that belonging is a multidimensional experience with several facets underpinning participants’ experiences. Students and academics identified several dimensions of belonging in relation to first year students’ experiences, as illustrated by two basic themes that sit under the organising theme: what it means to belong, and layers of belonging.
Basic theme: what it means to belong
This theme explores the idea that belonging at university is underpinned by feeling valued and connected. Academics and students agreed that having a sense of being valued by the university and a desire to have an active connection across all aspects of university life was important for students.
Belonging as a student was gained through a connection with the “vocation” (Student-Claire-Focus Group) or the course and career, and with people who will “be there” (Student-Claire-Focus Group) for them. Furthermore, support of academics was critical to gaining a sense of belonging. It was noted by academics and students, that when students feel they belong at university, they are actively engaged in their learning, and this sense of belonging in turn shapes their overall identity. Students can then “actually sort of relax and become themselves” (Staff-Brooke).
Belonging to their cohort, their course, their future profession, and their university was important for students. One academic noted that the “concept of acceptance” is part of the sense of belonging and goes “both ways” (Staff-Brooke).
Both academics and students agreed that the rapid change to online learning due to COVID-19, meant that developing a sense of belonging was challenged.
Basic theme: layers of belonging
This theme identified layers of belonging reflected in participants’ experiences. Peer, academic and professional layers each contributed to an overall sense of belonging and key examples are provided below.
Peers
Belonging to peers was described as “having that connection to someone that’s going through exactly the same thing as what you’re going through” (Student-Astrid-Focus Group). Students were concerned that when learning moved online that this sense of belonging would be jeopardised by less opportunities for in-person interaction.
Academics
Being connected to academics was perceived by students as directly impacting learning, with one student commenting: “…when they’re not connecting with the teacher, they’re not connecting with the content, they’re not connecting with the feedback. That’s when you develop this sense of feeling like you just don’t belong” (Student-Emily-Focus Group).
Academics perceived it was also important for students to develop a sense of belonging to the university community.
Profession
Belonging to a profession was identified as an important feature of belonging by academics and students. Studying a degree with a clear professional identity facilitated first year students to feel they belonged compared to those undertaking general health science degrees which may have multiple pathways and career options less directly aligned to first year studies.
One academic actively encouraged first year students to belong to their professional association as a way of fostering belonging in first years.
Organising theme—Individual experiences and challenges
This theme outlines that while there are similarities in participants’ experiences, individuals have unique contexts and factors shaping their experiences. Academics and students reflected upon personal impacts of the COVID-19 pandemic on their teaching or learning and how they responded as individuals to the ensuing challenges. Two basic themes emerged: Challenges of transition and recognising different learning preferences.
Basic theme —challenges of transition
This theme explored the significant challenges of transitioning to online teaching and learning. For some students, the transition to online learning offered potential benefits of flexibility and reduced travel time. Two of the four students in the focus groups opted for online learning opportunities available in other subjects of study prior to the pandemic to efficiently manage their study and external commitments. Nonetheless, the pandemic brought a raft of personal challenges that diminished these expected benefits. Covid-related changes to family employment, reduced access to childcare support and non-optional home schooling presented new concerns.
Clearly, students missed the opportunity to focus attention on their learning needs when balancing childcare demands and home-schooling during lockdowns.
Unlike a conventional online courses where students choose or plan to be online, the sudden, unexpected, and unplanned move to online study was prefaced by a short period (four weeks) of in-person class time. This initial in-person time was identified as being key to relationship building.
Academics identified positive experiences and challenges during the transition to online learning. The rapid change presented a problem to be solved and individuals could “embrace it and to work effectively…as a team” (Staff-Jane). Quickly strategizing and responding to the demands of online learning required team knowledge, experience, and support. Hence, enhanced team culture was a further positive for academics, being “present for each other” (Staff-Brooke).
Basic theme : recognising different learning preferences
This theme identifies experiences of online learning influenced by personal attributes, individual expectations and learning preferences. Such key factors impacted students’ capacity to maintain focus on academic goals after the rapid change to online learning. Some students reflected that barriers were not solely a feature of online learning environments, reporting that competing priorities, including work commitments and limited contact time with staff as pre-existing challenges to belonging. However, some students directly attributed their limited engagement and reduced motivation to the online learning environment.
Students suggested that active engagement “comes down to personality” (Student-Astrid- Focus Group). If a student was not shy they were comfortable to come forward and participate online. Some students perceived clear links between personal discipline, engagement, commitment, and achievement in online learning environments.
Further, students perceived effective (and ineffective) online group functioning reflected personalities of individual members, with some groups/personalities seen as being able to organise whilst other groups lacked leadership and cohesion.
Students who perceived themselves as active engagers reported being drawn towards other students who demonstrated motivation to interact and learn. Other students perceived their personalities or learning preferences were misaligned with the expectations of belonging in online learning environments and focussed upon tasks rather than connection.
Academics recognised student diversity and a need to reflect and re-evaluate expectations of students in online environments. They accepted that some students may be quietly engaging and learning to belong, but this was harder to observe in online compared to in-person learning environments.
Organising theme—relationships are central to belonging
This theme identified the relationship between all parties as a fundamental aspect of creating a sense of belonging. Two basic themes were influential in shaping perceptions of how relationships and connections contribute to belonging: collaboration with peers is fundamental, and effective and regular communication with staff is necessary.
Basic theme —collaboration with peers is fundamental
This theme revealed collaboration with student peers was a key element of creating a sense of belonging. The degree of social interaction with student peers and opportunities to create friendships contributed to feelings of belonging. Accordingly, students found it problematic when peers neglected to turn cameras on during classes, making interaction very difficult. Visualisation of peers and use of cameras in online classes impacted students’ opportunities to get to know each other.
Challenges posed by online learning were further highlighted in the student survey through a focus on non-academic aspects of university and campus life. Typically, university campuses offer interactional opportunities through clubs, sport, and shared spaces to learn and socialise. Campus life, students suggested, may facilitate learning and personal development. Absence of this type of interaction was linked to barriers in developing friendships and consequently a lesser sense of belonging as reflected in Additional file 1.
Basic theme— communication with academics is necessary
This theme outlined that communicating with academics was a key component of creating a sense of belonging. With less opportunities for peer support, there was stronger reliance on the academic-student connection, although students reported positive and negative interactions with academics during online learning.
Positive interactions and individualised communication with academics enhanced student sense of satisfaction and belonging. Furthermore, students in the focus groups reported a feeling of trust and a bond created by a shared challenge. Survey responses echoed this sentiment, noting that academics were “non-judgmental and supportive” (Student Survey 18) and created a sense of camaraderie. However, when students perceived impersonal communication from academics, they felt less connected or believed that teaching had become a “transaction” (Student-Astrid- Focus Group). Perceived levels of enthusiasm and engagement from academics influenced student’s perceptions of connection and belonging.
Students identified the online environment as a barrier to communication with academics. While systematic and university level communication was perceived as a useful source of information, students prioritised individualised communication from academic staff as key to belonging.
Academics concurred that effective communication was challenged in online environments, missing non-verbal cues and responsivity that characterises a classroom environment. Although the online learning environment provides an opportunity for academics to connect professionally with students, there were students who left their cameras off, with one academic noting they didn’t push this issue because there are many reasons for students choosing this option.
Organising theme: reconceptualising teaching and learning
This theme reveals how academics and students reconceptualised their expectations and modes of teaching and learning, to manage the crisis. It was not easy for academics or students, and many strategies were employed to make it work, with two basic themes emerging: challenges to online teaching and learning, and strategies to engage and connect.
Basic theme: challenges of online teaching and learning: “how do I make this work?”
This theme outlined many challenges faced by both academics and students during a rapid change to online mode. With the rapid change to online learning, academics asked themselves, ‘How do I make this work?’.
Managing workload
Academics reported their workload increased significantly, and they “found it a juggling act” (Staff-Louise) to meet their teaching requirements. Administrative loads consequently increased when reduced in-person contact with students led to more electronic communication. Academics needed to up-skill in online teaching in a short time frame and perceived this responsibility as all encompassing.
The rapid switch to online learning attracted significant academic workload, implementing and adapting content to see how material “might play out in a Zoom environment…[where]…everything takes longer” (Staff-Natalie).
Some students noticed a temptation to disengage from online learning, which meant balancing their workload and study demands became a challenge as they also faced significant workload and stressors in their personal lives due to COVID-19.
Class dynamics
Academics and students spoke about the change to classroom dynamics. The online environment was noted as being one in which it was difficult to read the room to see how students were progressing with their work. Others tried to use humour to enliven a class, only to have the Zoom frame freeze, killing the mood they were trying to create. Hence, staff felt teaching online was less conversational, flexible and responsive compared to face-to-face. Moreover, academics missed hands-on practical elements; a big shift for some programs.
Technological challenges
Academics learnt new skills quickly, but often these skills would be challenged when technology failed. Some academics reported a sense of vulnerability due to technological ineptitude but acknowledged that making mistakes in front of students could humanise the experience. Academics also acknowledged that some students did not have adequate technological resources to meet changes in their learning requirements when classes were placed online.
Basic theme: strategies to engage and connect
This theme reflected the strategies academics and students employed to remain engaged and connected. Academics worked hard to enhance online learning and hoped to connect with students and engage them in activities. Students too were active and appreciated academics’ efforts to facilitate engagement and connection. Underlying many of the strategies adopted by academics was a deep concern for student welfare during this time. Therefore, many academics aimed to ensure students were engaged and connected with each other and with the academic team. Academics built in small group opportunities during online teaching so students could connect, learn, and socialise.
Staff also spoke about informing students they could contact staff for support. One staff member described crossing the divide and actively discouraging a ‘them and us’ dynamic between students and staff.
A variety of teaching tools were identified by staff to build connection and promote engagement. Such tools included interactive quizzes, ice breakers activities, integrating reflective practices into activities and ‘drop in’ sessions. Staff also encouraged students to establish social media groups or other group experiences outside the classroom. Some staff members arrived early to zoom classes and left late to enable students to connect informally.
Students appreciated staff attempts to provide these activities. Students found these initiatives helpful, recognising staff placed effort into knowing students personally and focussing on student wellbeing and achievement. Students cited examples of provision of extra resources, mini-lectures, additional question and answer sessions, and fast response times to student queries. Students also initiated their own engagement strategies, including using group and personal messaging over platforms such as Facebook messenger.
Collaborators GBDS. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021;20(10):795–820.
Joseph P, Leong D, McKee M, Anand SS, Schwalm JD, Teo K, et al. Reducing the global burden of cardiovascular disease, part 1: the epidemiology and risk factors. Circ Res. 2017;121(6):677–94.
Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart disease and stroke Statistics-2022 update: a report from the American Heart Association. Circulation. 2022;145(8):e153–639.
D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham heart study. Circulation. 2008;117(6):743–53.
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ. 2007;335(7611):136.
Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European Society of Cardiology and Other Societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–81.
Majithia S, Tham YC, Chong CCY, Yu M, Cheung CY, Bikbov MM, et al. Retinal nerve fiber layer thickness and rim area profiles in Asians: pooled analysis from the Asian eye epidemiology consortium. Ophthalmology. 2022;129(5):552–61.
Shin JY, Lee J, Lee CJ, Park S, Byeon SH. Association between localised retinal nerve fibre layer defects and cardiovascular risk factors. Sci Rep. 2019;9(1):19340.
Lamparter J, Schmidtmann I, Schuster AK, Siouli A, Wasielica-Poslednik J, Mirshahi A, et al. Association of ocular, cardiovascular, morphometric and lifestyle parameters with retinal nerve fibre layer thickness. PLoS One. 2018;13(5):e0197682.
Zhang Q, Wang D, Wang A, Zhang S, Pan Y, Li Y, et al. Relationship of ideal cardiovascular health metrics with retinal vessel calibers and retinal nerve fiber layer thickness: a cross-sectional study. BMC Cardiovasc Disord. 2018;18(1):187.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779.
Chua SYL, Khawaja AP, Dick AD, Morgan J, Dhillon B, Lotery AJ, et al. Ambient air pollution associations with retinal morphology in the UK Biobank. Invest Ophthalmol Vis Sci. 2020;61(5):32.
Chua SYL, Lascaratos G, Atan D, Zhang B, Reisman C, Khaw PT, et al. Relationships between retinal layer thickness and brain volumes in the UK Biobank cohort. Eur J Neurol. 2021;28(5):1490–8.
Khawaja AP, Chua S, Hysi PG, Georgoulas S, Currant H, Fitzgerald TW, et al. Comparison of associations with different macular inner retinal thickness parameters in a large cohort: the UK Biobank. Ophthalmology. 2020;127(1):62–71.
Ko F, Muthy ZA, Gallacher J, Sudlow C, Rees G, Yang Q, et al. Association of retinal nerve fiber layer thinning with current and future cognitive decline: a study using optical coherence tomography. JAMA Neurol. 2018;75(10):1198–205.
Patel PJ, Foster PJ, Grossi CM, Keane PA, Ko F, Lotery A, et al. Spectral-domain optical coherence tomography imaging in 67 321 adults: associations with macular thickness in the UK Biobank study. Ophthalmology. 2016;123(4):829–40.
Keane PA, Grossi CM, Foster PJ, Yang Q, Reisman CA, Chan K, et al. Consortium UKBEV: optical coherence tomography in the UK Biobank study – rapid automated analysis of retinal thickness for large population-based studies. PLoS One. 2016;11(10):e0164095.
Early Treatment Diabetic Retinopathy Study Research Group. Grading diabetic retinopathy from stereoscopic color fundus photographs–an extension of the modified Airlie house classification. ETDRS report number 10. Ophthalmology. 1991;98(5 Suppl):786–806.
Fan M, Sun D, Zhou T, Heianza Y, Lv J, Li L, et al. Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants. Eur Heart J. 2020;41(11):1182–9.
Said MA, Verweij N, van der Harst P. Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK Biobank study. JAMA Cardiol. 2018;3(8):693–702.
Yates T, Zaccardi F, Dhalwani NN, Davies MJ, Bakrania K, Celis-Morales CA, et al. Association of walking pace and handgrip strength with all-cause, cardiovascular, and cancer mortality: a UK Biobank observational study. Eur Heart J. 2017;38(43):3232–40.
Zhang P, Guo D, Xu B, Huang C, Yang S, Wang W, et al. Association of serum 25-hydroxyvitamin D with cardiovascular outcomes and all-cause mortality in individuals with prediabetes and diabetes: results from the UK Biobank prospective cohort study. Diabetes Care. 2022;45(5):1219–29.
Ramakrishnan R, Doherty A, Smith-Byrne K, Rahimi K, Bennett D, Woodward M, et al. Accelerometer measured physical activity and the incidence of cardiovascular disease: evidence from the UK Biobank cohort study. PLoS Med. 2021;18(1):e1003487.
Wan EYF, Fung WT, Schooling CM, Au Yeung SL, Kwok MK, Yu EYT, et al. Blood pressure and risk of cardiovascular disease in UK Biobank: a Mendelian randomization study. Hypertension. 2021;77(2):367–75.
Diniz-Filho A, Abe RY, Zangwill LM, Gracitelli CP, Weinreb RN, Girkin CA, et al. Association between intraocular pressure and rates of retinal nerve fiber layer loss measured by optical coherence tomography. Ophthalmology. 2016;123(10):2058–65.
Wang W, Chen Y, Kun X, Gong X, Liu H, Wei D, et al. Flow and geometrical alterations in retinal microvasculature correlated with the occurrence of diabetic retinopathy: evidence from a longitudinal study. Retina. 2022;42(9):1729–36.
Lim HB, Shin YI, Lee MW, Park GS, Kim JY. Longitudinal changes in the peripapillary retinal nerve fiber layer thickness of patients with type 2 diabetes. JAMA Ophthalmol. 2019;137(10):1125–32.
Sohn EH, van Dijk HW, Jiao C, Kok PH, Jeong W, Demirkaya N, et al. Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proc Natl Acad Sci U S A. 2016;113(19):E2655–64.
Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017;318(14):1377–84.
Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann Intern Med. 2014;160(2):122–31.
Pencina MJ, D’Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11–21.
Tsimikas S, Willeit P, Willeit J, Santer P, Mayr M, Xu Q, et al. Oxidation-specific biomarkers, prospective 15-year cardiovascular and stroke outcomes, and net reclassification of cardiovascular events. J Am Coll Cardiol. 2012;60(21):2218–29.
Kim M, Park KH, Kwon JW, Jeoung JW, Kim TW, Kim DM. Retinal nerve fiber layer defect and cerebral small vessel disease. Invest Ophthalmol Vis Sci. 2011;52(9):6882–6.
Wang D, Li Y, Zhou Y, Jin C, Zhao Q, Wang A, et al. Asymptomatic carotid artery stenosis and retinal nerve fiber layer thickness. A community-based, observational study. PLoS One. 2017;12(5):e0177277.
Xu L, Zhou JQ, Wang S, Wang YX, You QS, Yang H, et al. Localized retinal nerve fiber layer defects and arterial hypertension. Am J Hypertens. 2013;26(4):511–7.
Hayreh SS, Jonas JB. Appearance of the optic disk and retinal nerve fiber layer in atherosclerosis and arterial hypertension: an experimental study in rhesus monkeys. Am J Ophthalmol. 2000;130(1):91–6.
Lee MW, Lee WH, Park GS, Lim HB, Kim JY. Longitudinal changes in the peripapillary retinal nerve fiber layer thickness in hypertension: 4-year prospective observational study. Invest Ophthalmol Vis Sci. 2019;60(12):3914–9.
Wan P, Su W, Zhang Y, Li Z, Deng C, Li J, et al. LncRNA H19 initiates microglial pyroptosis and neuronal death in retinal ischemia/reperfusion injury. Cell Death Differ. 2020;27(1):176–91.
Wagner SK, Fu DJ, Faes L, Liu X, Huemer J, Khalid H, et al. Insights into systemic disease through retinal imaging-based oculomics. Transl Vis Sci Technol. 2020;9(2):6.
Vadala M, Castellucci M, Guarrasi G, Terrasi M, La Blasca T, Mule G. Retinal and choroidal vasculature changes associated with chronic kidney disease. Graefes Arch Clin Exp Ophthalmol. 2019;257(8):1687–98.
Pontikos, Siegfried KW, Balaskas K, Faes L, Kortuem K, Fasler K, et al. Comment on: trends in retina specialist imaging utilization from 2012 to 2016 in the United States Medicare fee-for-service population. Am J Ophthalmol. 2020;211:229.
Robson, Dostal I, Sheikh A, Eldridge S, Madurasinghe V, Griffiths C, et al. The NHS health check in England: an evaluation of the first 4 years. BMJ Open. 2016;6(1):e008840.
Ran A, Cheung CY. Deep learning-based optical coherence tomography and optical coherence tomography angiography image analysis: an updated summary. Asia Pac J Ophthalmol (Phila). 2021;10(3):253–60.
Lains I, Wang JC, Cui Y, Katz R, Vingopoulos F, Staurenghi G, et al. Retinal applications of swept source optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Prog Retin Eye Res. 2021;84:100951.
Ho H, Tham YC, Chee ML, Shi Y, Tan NYQ, Wong KH, et al. Retinal nerve fiber layer thickness in a multiethnic normal Asian population: the Singapore epidemiology of eye diseases study. Ophthalmology. 2019;126(5):702–11.
Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Circulation. 2019;140(11):e596–646.
This cross-sectional analyze was executed using the Q methodology during the subsequent six techniques utilizing Barry and Proops method [19].
Phase 1 and 2: defining the concourse
At this phase, a concourse space was fashioned with the identification of the matter or idea of the analyze. The offered sights on the situation elevated for the concourse can be formed from a assessment of texts and authorities in this field [19].
In this review, the matter and notion for the concourse were being the problems of on the web education and learning all through the COVID-19 pandemic. The concourse provided a collection of various supplies associated to the investigation subject that was mentioned among the students. The pupils (P-established) who also experienced contributed before to the enhancement of the initial set of statements. Thirty-one particular learners participated in semi-structured interviews, and we tried using to establish their subjectivity about the investigation matter working with the Q approach [20].
In this analyze, the concourse (sample of people) provided college students of the University of Health care Sciences (paramedical learners) who had enough data about online training for the duration of the COVID-19 pandemic.
Stage 3: screening and assortment of statements (Q-sample)
In the course of the semi-structured interviews with 31 learners, 70 statements ended up extracted about the perceived difficulties of online schooling. The Q goods ended up picked quite diligently so that things did not overlap, and at the exact time, no point of view need to be missing. Thus, the variety process usually takes the most time and hard work of all the measures of the Q methodology. For that reason, exploration group taken out related unrelated, and ambiguous statements from the Q set. Eventually, 50 statements had been selected.
Phase 4: picked P-established
Students who participated in the concourse (interviews) had been chosen as a sample of individuals to take part in sorting in the Q examine (P-established). In the present study, learners have been picked by purposive sampling to include college students who experienced an academic, skilled, experimental marriage or prior information about the subject of study. This range of samples designed the contributors with much more varied mentalities enter the examine. It is advised that in Q experiments, the variety of members to form statements should really be less than the number of statements close to the analyze issue [21]. In the present examine, the range of individuals who rated the troubles of on the web education packages was 31 (Table 1).
Desk 1 The Q-established statements and issue arrays in the analyze of worries on the net instruction among pupils
Phase 5: Q-sort
At this stage, the normal distribution table in the type of a Likert scale from − 5 to + 5 was developed offline. Suggestions on distributing the expressions on the typical distribution table have been delivered. In the first phase, the intent of the examine is the quantity of statements picked by means of the interview. In the second phase, position the statements in 3 columns: “I agree”, “I have no viewpoint,” and “I disagree. In the third phase, the statements (necessary) are distributed in the ordinary Likert distribution diagram (− 5 to 5+), detailing the motive for deciding upon the two ends of the Likert scale from their issue of look at and lastly moving into the demographic facts. So, in Q, the sorting process is subjective [19]. In other text, sorting things in the regular distribution make it possible for each and every participant to existing their internal standpoint via sorting.
Stage 6: examination and interpretation of components
Students’ knowledge obtained from Q sorting were being entered into PQ-Method program model 2.35. The system of analysis and interpretation was executed in three levels: (a) identification of factors, (b) conversion of elements into element arrays (c) interpretation of variables applying element arrays.
A)
Element Identification
The extraction of components in PQ-Method software was performed by the subsequent sequential techniques: (a) principal element examination, (b) identification of latent things, (c) varimax rotation and analysis of loading aspects for unique values earlier mentioned 1.00, d) estimation of the percentage of variance described by the discovered variables and (e) differentiation of interpretable components with at minimum two correlated Q forms [22].
B)
Transform element to element arrays
The correlation among each Q sort and just one discovered element indicates the degree of interaction in between the Q sorts and the recognized factors [19, 23]. The handbook flagging in PQ-Approach program was used for this examine. The correlation coefficients of at the very least .364 had been thought of as the reduce-off issue (the absolute value of the issue load is increased than ((frac2.58sqrtN)). That factor load was 99{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} significant, respectively, and the value of N was equivalent to the quantity of Q statements (N = 50). Sorted for identified elements [24]. Specs specified on a component are employed to create a aspect array for that element. The variable array represents the sorting of that factor (point of watch) applying z-scores. The component array for each individual issue determined the degree to which each individual expression was in the spectrum, so a extra precise interpretation of just about every component (subjectivity) was attained according to the placement of every single expression. (P-worth< 0.05 vs. 0.01) is also determined from the Z score to distinguish expressions [25].
III)
Factor interpretation using factor arrays
Distinct Q expressions were identified, and factors were interpreted textually. The defining expressions for a factor were those that had a rating value of “+ 5”, “+ 4”, “4-,” 5- “in factor arrays that had different scores (P < 0.05) in a given factor Compared to their scores on other factors, the post-P-set interview was conducted at the end of Q sorting to confirm the diagnosis and interpretation of item subgroups among the identified factors.
Obesity in children is a general public worry around the globe and is affiliated with form 2 diabetic issues, hypertension, and an amplified hazard of obesity in adulthood [1, 2]. For instance, in Japanese faculty-aged small children, 11.1{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of boys and 8.8{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of women aged 11 years had been categorized as obese in 2019 [3]. In comparison to other formulated international locations, amounts of being overweight in Japanese college-aged little ones are low [4] nevertheless, the percentage has grown in the very last 10 many years [3]. In particular in women, elementary university-age pupils are additional likely to be overweight or obese than junior large university or high school-age college students [3]. Therefore, blocking weight problems in kids is crucial for their upcoming wellness.
Excessive sedentary habits is associated with weak wellness and can final result in improved adiposity, worse cardiometabolic overall health and health and fitness, impaired behavioral perform/professional-social behavior, and lessened rest duration [5]. For small children, various recent physical action pointers [6, 7] recommend recreational display screen time of no much more than 2 h for every day (i.e., seeing television [T.V.], electronic video clip discs, or videos, taking part in T.V. video games, or utilizing personal computers or the web) and staying away from prolonged periods of sitting down. However, youngsters commit much too much time on their recreational display screen time around the world [8]. For instance, in the United States, 66{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of youngsters spend at minimum 2 h of display time per working day [9]. In Japan, approximately 60{e4f787673fbda589a16c4acddca5ba6fa1cbf0bc0eb53f36e5f8309f6ee846cf} of small children have been found to exceed the 2 h for each day mark of monitor time [10].
Moms and dads perform an crucial part in children’s everyday determination-earning through modeling, regulations or restrictions, social guidance, and co-participation [11, 12]. Preceding review reports have proven that parents’ monitor time is positively correlated with children’s display time [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27], and co-viewing with mom and dad has been associated with elevated display time in youngsters [28, 29]. Additionally, the affect on children’s display screen time seems to be dependent on the sex of the guardian, as a former study noted that mothers’ screen-based mostly behaviors showed a positive correlation with children’s screen time [17, 28, 29]. However, few research have regarded as gender variances in parental roles. Scientific tests that have examined equally the father’s and mother’s affect on children’s sedentary actions report that as opposed to the father’s sedentary conduct, the mother’s sedentary behavior influences the child’s sedentary habits a lot more [28, 29]. Xu et al. [30] concluded that cutting down parents’ monitor time could minimize their child’s screen time. Therefore, examining the effects of both equally fathers’ and mothers’ display screen time on little ones is important.
In addition to the influence of the parents’ gender, it has been described that the affect of the parents’ screen time on children’s display time may differ concerning weekdays and weekends [19, 27]. Jago et al. (2014) [27] concluded that associations noticed amongst father or mother and baby monitor-viewing ended up different involving weekdays and the weekend they confirmed that on a weekday, young children had been 3.4 situations additional most likely to exceed 2 h of display screen viewing if their father viewed T.V. for at minimum 2 h for every day, when for a weekend day, kids ended up 4.8 times extra probable. There were being very similar associations for mothers small children ended up 3.7 occasions far more possible to exceed 2 h of display screen viewing if their mother watched T.V. for at the very least 2 h per working day on a weekday, although young children were 4.7 instances more possible for a weekend. On the other hand, to our understanding, only a couple of scientific tests have examined the differentiation involving weekdays and weekends [18, 19, 27].
The indirect effects and the toughness of paternal and maternal display time on children’s screen time and system mass index (BMI) have not been examined. Even so, some reports have examined each of these variables specifically, this kind of as parents’ screen time and children’s display time [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] or children’s display time and BMI [5]. Considering the influence of the behaviors of each father and mother on children in serious everyday living, parental behaviors might effects children’s monitor time and BMI, and ideas for distinct interventions to strengthen children’s health and fitness may well be possible as a result of investigation. Hence, the present review examined how the direct and indirect outcomes of parents’ and children’s monitor time behaviors influenced children’s BMI amongst Japanese elementary school small children.
Ferrer A, Formiga F, Sanz H, de Vries OJ, Badia T, Pujol R, et al. Multifactorial assessment and targeted intervention to reduce falls among the oldest-old: a randomized controlled trial. Clin Interv Aging. 2014;9:383–93.
Esain I, Rodriguez-Larrad A, Bidaurrazaga-Letona I, Gil SM. Health-related quality of life, handgrip strength and falls during detraining in elderly habitual exercisers. Health Qual Life Outcomes. 2017;15(1):226.
Haagsma JA, Olij BF, Majdan M, van Beeck EF, Vos T, Castle CD, et al. Falls in older aged adults in 22 European countries: incidence, mortality and burden of disease from 1990 to 2017. Inj Prev. 2020;26(Supp 1):i67.
Statbel. Kerncijfers – Statistisch overzicht van België. 2020.
James SL, Lucchesi LR, Bisignano C, Castle CD, Dingels ZV, Fox JT, et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017. Inj Prev. 2020;26(Suppl 2):i3.
WHO. Global report on falls prevention in older age. Geneva: World Health Organization; 2008.
Lu Z, Lam F, Leung J, Kwok T. The U-Shaped relationship between levels of bouted activity and fall incidence in community-dwelling older adults: a prospective cohort study. J Gerontol A Biol Sci Med Sci. 2020;75(10):e145–51.
Aranyavalai T, Jalayondeja C, Jalayondeja W, Pichaiyongwongdee S, Kaewkungwal J, Laskin J. Association between walking 5000 step/day and fall incidence over six months in urban community-dwelling older people. BMC Geriatr. 2020;20(1):194.
Gazibara T, Kurtagic I, Kisic-Tepavcevic D, Nurkovic S, Kovacevic N, Gazibara T, et al. Falls, risk factors and fear of falling among persons older than 65 years of age. Psychogeriatr. 2017;17(4):215–23.
Pérez-Ros P, Martínez-Arnau F, Orti-Lucas R, Tarazona-Santabalbina F. A predictive model of isolated and recurrent falls in functionally independent community-dwelling older adults. Braz J Phys Ther. 2019;23(1):19–26.
Carrasco C, Tomas-Carus P, Bravo J, Pereira C, Mendes F. Understanding fall risk factors in community-dwelling older adults: A cross-sectional study. Int J Older People Nurs. 2020;15(1):e12294.
Lahiri A, Jha S, Chakraborty A. Elders suffering recurrent injurious falls: causal analysis from a rural tribal community in the eastern part of India. Rural Remote Health. 2020;20(4):6042.
Zhou H, Peng K, Tiedemann A, Peng J, Sherrington C. Risk factors for falls among older community dwellers in Shenzhen. China Injury Prevent. 2019;25(1):31–5.
Kamińska M, Brodowski J, Karakiewicz B. Fall risk factors in community-dwelling elderly depending on their physical function, cognitive status and symptoms of depression. Int J Environ Res Public Health. 2015;12(4):3406–16.
Wang L, Wang X, Song P, Han P, Fu L, Chen X, et al. Combined depression and malnutrition as an effective predictor of first fall onset in a chinese community-dwelling population: a 2-year prospective cohort study. Rejuvenation Res. 2020;23(6):498–507.
Tanaka T, Matsumoto H, Son B, Imaeda S, Uchiyama E, Taniguchi S, et al. Environmental and physical factors predisposing middle-aged and older Japanese adults to falls and fall-related fractures in the home. Geriatr Gerontol Int. 2018;18(9):1372–7.
Stewart Williams J, Kowal P, Hestekin H, O’Driscoll T, Peltzer K, Yawson A, et al. Prevalence, risk factors and disability associated with fall-related injury in older adults in low- and middle-incomecountries: results from the WHO Study on global AGEing and adult health (SAGE). BMC Med. 2015;13(1):147.
Cheng M, Chang S. Frailty as a risk factor for falls among community dwelling people: evidence from a meta-analysis. J Nursing Scholarship :Off Publ Sigma Theta Tau Int Honor Soc Nursing. 2017;49(5):529–36.
Sezgin D, O’Donovan M, Cornally N, Liew A, O’Caoimh R. Defining frailty for healthcare practice and research: A qualitative systematic review with thematic analysis. Int J Nurs Stud. 2019;92:16–26.
Huang C-Y, Lee W-J, Lin H-P, Chen R-C, Lin C-H, Peng L-N, et al. Epidemiology of frailty and associated factors among older adults living in rural communities in Taiwan. Arch Gerontol Geriatr. 2020;87:103986.
Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2013;16(1):441.
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.
Greene BR, Redmond SJ, Caulfield B. Fall risk assessment through automatic combination of clinical fall risk factors and body-worn sensor data. IEEE J Biomed Health Inform. 2017;21(3):725–31.
Cella A, De Luca A, Squeri V, Parodi S, Vallone F, Giorgeschi A, et al. Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults. PLoS ONE. 2020;15(6):e0234904.
Menezes M, de Mello Meziat-Filho NA, Araújo CS, Lemos T, Ferreira AS. Agreement and predictive power of six fall risk assessment methods in community-dwelling older adults. Arch Gerontol Geriatr. 2020;87:103975.
Zhang L, Ding Z, Qiu L, Li A. Falls and risk factors of falls for urban and rural community-dwelling older adults in China. BMC Geriatr. 2019;19(1):379.
Rokach L, Maimon O. data mining with decision trees. World Sci. 2013;328.
Van Rossum G, Drake FL, Jr. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0:fundamental algorithms for scientific computing in python. Nat Methods. 2020;17:261–72.
McKinney W. Data structures for statistical computing in python. in: van der walt s, millman j, editors. Proc 9th Python Sci Conf. 2010;56–61.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12(85):2825–30.
De Witte N, Gobbens R, De Donder L, Dury S, Buffel T, Schols J, et al. The comprehensive frailty assessment instrument: development, validity and reliability. Geriatr Nurs. 2013;34(4):274–81.
Lambotte D. Care and support in later life: A study on the dynamics of care networks of frail, community-dwelling older adults. Brussels: ASP / VUBPRESS; 2018.
Kojima G, Kendrick D, Skelton D, Morris R, Gawler S, Iliffe S. Frailty predicts short-term incidence of future falls among British community-dwelling older people: a prospective cohort study nested within a randomised controlled trial. BMC Geriatr. 2015;15:155.
Jehu DA, Davis JC, Falck RS, Bennett KJ, Tai D, Souza MF, et al. Risk factors for recurrent falls in older adults: A systematic review with meta-analysis. Maturitas. 2021;144:23–8.
Almada M, Brochado P, Portela D, Midão L, Costa E. Prevalence of falls and associated factors among community-dwelling older adults: a cross-sectional study. J Filty Ageing. 2021;10(1):10–6.
Byun M, Kim J, Kim JE. Physical and psychological factors contributing to incidental falls in older adults who perceive themselves as unhealthy: a cross-sectional study. Int J Environ Res Public Health. 2021;18(7):3738.
