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Abstract

Objective

The purpose of this study is to examine how care workers’ characteristics are related to the perceived benefits and drawbacks of using videoconferencing in the care of older people. The factors chosen for this study are: age, education, perceived information and communication technology (ICT) support, interest in technology, ICT skills and possible prior experience of using videoconferencing tools.

Methods

Our data source was the second wave (2021) of the University of Jyväskylä survey on elder care work (N = 3607), collected from four large trade unions in Finland. We used a multinomial logistic regression to group respondents according to their experiences of technology use and a path analysis to estimate the effects of care workers’ characteristics and prior experiences on the perceived benefits of videoconferencing tools in eldercare work.

Results

We found that the personal characteristics of a care worker are associated with both the use of videoconferencing and its perceived benefits and drawbacks. In addition, we discovered that prior use of videoconferencing tools, especially in direct care work, affects the perceived benefits of them. Those who have used videoconferencing in direct care work perceive them as more beneficial for both their own work and for clients.

Conclusions

Based on these results, we suggest that healthcare and social welfare organizations pay attention to the characteristics of care workers, especially when estimating the necessary amount of ICT support. More ICT support should be targeted to those with a lower level of education and higher age.

Keywords

videoconferencing care worker eldercare personal characteristics user experience

Introduction

Advances in information and communication technology (ICT) have significantly transformed eldercare work, which in the Finnish context covers both social and health care professionals. In the ICT-driven delivery of eldercare services, telehealth has emerged as a promising solution to meet the changing needs of an ageing population and to provide more cost-effective care.^1–4 As an essential part of telehealth, videoconferencing refers to the use of real-time audio-visual communication devices to facilitate remote interactions between care workers and older people in both social care and health care for older adults with care needs.^5,6 The increased use of telehealth technologies has been claimed to support older people to live independently, to promote active and assisted living and to improve access to health-related services by changing the dynamics of care delivery.^7,8 Care workers’ perceptions of their working conditions have changed, with a growing sense of urgency and mounting work-related pressure. These heightened work pressures are also related to an increase in non-direct care responsibilities such as more frequent work-related meetings.^9–16 At the same time, there has been a notable increase in the adoption of telehealth technologies, a development that has been emphasized due to the demands created by the COVID-19 pandemic.^15,17–22 Despite the current trend towards the introduction of new telehealth services in social and health care, the use of videoconferencing in eldercare, particularly from the perspective of care workers, has not been thoroughly explored. The current study aims to fill this gap by exploring how care worker related factors are associated with the perceived benefits and drawbacks of videoconferencing in the care of older people.

Prior research shows that the implementation of ICT in healthcare comes not without challenges. It has been shown that in the rapidly changing context of healthcare, approximately half of the ICT projects fail due to financial, social, cultural, managerial and technical issues.^23–25 Typical causes of such failures include a lack of end user involvement in the implementation process, unclear definitions of the expected benefits of the technology, user perceptions of the technology, possible user resistance and the complexity of the actual care environment.^23,25–28 On the contrary, user involvement in the development and implementation of these technologies has been shown to be a key success factor.^23,29–33 All this suggests that both eldercare workers and people receiving care, as end users of videoconferencing tools, have a central role to play in when considering the successful use of videoconferencing tools in the care of older people.

The Technology Acceptance Model (TAM) and its extensions have been widely used to predict individual acceptance and use of technology in healthcare.^34,35 However, TAMs suit poorly in organizational contexts³⁶ where the use of technology is involuntary³⁷ and the users’ work environment, cultural background and individual characteristics have not been thoroughly considered.^34,36–40 In this study, we assume that the use of videoconferencing tools is required by the organization, and we examine how care worker-related factors – namely age, education, perceived ICT support, interest in technology and ICT skills and possible previous experience of using videoconferencing tools – are related to the perceived benefits of videoconferencing in care for older people. The research questions defined for the study are:

RQ1: Which personal characteristics of a care worker are related to the use of videoconferencing?

RQ2: Are there differences in the perceived benefits depending on prior experience of videoconferencing?

RQ3: How are the personal characteristics of a care worker associated with the perceived benefits of videoconferencing use?

Previous research shows that age is strongly associated with ICT skills, usage and attitudes towards new technologies.^38,41–43 On the one hand, older age has been perceived to relate to lower ICT skills,⁴⁴ and on the other hand, generations born in or after the 1980s have been found to have an advantage in ICT skills.⁴⁵ The literature has also highlighted that education has a significant impact on a person's attitude towards the use of ICT and, consequently, on their willingness to use digital technology – more education means a more positive attitude.⁴² In addition to attitudes, education is related the ability to solve problems in technology-rich environments.^43,46,47 As technology has become a more fundamental part of modern working life, it also requires workers with a lower level of education, such as vocational training, to engage in innovative forms of problem solving.

Regarding ICT support in the context of eldercare, prior research is limited. Adequate user training and support have been identified as key factors in the successful use of ICT.⁴⁸ Similarly, there is little research on ICT skills and interest in technology in the field of eldercare work. However, these issues have been widely studied, for example, in the field of education research. These studies show that ICT skills, or individuals’ perceptions of their skills, and interest in technology are positively correlated,⁴⁹ which can be explained by the social use of ICT.⁵⁰ There is also a positive relationship between performance expectancy and interest in using technology, and between interest in using technology and the conditions that support its use.³⁸

Based on the literature presented above, we created a hypothesized model of factors and their relationships to the perceived benefits of videoconferencing tools. We hypothesize that age is negatively related to education level and interest in technology but positively related to ICT support and the lack of ICT skills. Likewise, we assume that education is positively related to interest in technology but negatively related to perceived ICT support and the lack of ICT skills. We also expect perceived ICT support to have a negative effect on the respondent's lack of ICT skills but a positive effect on the perceived benefits of videoconferencing tools. Furthermore, we expect a lack of ICT skills to be negatively related to interest in technology and the perceived benefits of videoconferencing tools. We hypothesize that both interest in technology and education level are positively related to the perceived benefits of videoconferencing tools.

Recent research has reported several benefits of videoconferencing in eldercare. Videoconferencing can be used to provide remote health care for older adults in care homes through assessment, management of health care, clinical support and diagnostics.^51,52 In addition, the use of videoconferencing has been found to reduce the social isolation of older people by offering opportunities to connect with family and friends, especially in unconventional conditions such as stay-at-home orders.^53,54 It can also reduce the inconvenience of travel for clients and thus ensure better continuity of care.^53,55 From a care worker's perspective, remote technology can distance them from potentially poor working conditions, such as physical and psychological violence.⁵⁶ Previous research also reports some limitations related to the use of videoconferencing in eldercare. There are sometimes limitations in communication cues as well as a limited view or low camera quality,⁵⁷ leading to concerns of not being able to fully identify and understand clients’ medical issues, among others.^58,59

Existing research does not provide a clear answer to the question of how exactly videoconferencing should be used in care alongside traditional in-person care to benefit clients, staff and administration and to ensure that care meets high ethical standards.^58–61 For example, in a randomized controlled trial study, a group of patients visited remotely had better blood pressure results than a group patients visited in-person at their homes.⁶¹ A review of 12 research articles indicated that, generally, both clients and nurses were pleased with the quality of videoconferencing care.⁵⁴ In many previous studies,^57,62 videoconferencing has been combined with in-person visits instead of replacing them, which might partly explain the high satisfaction with the studied technology. Older people have been shown to prefer videoconferencing over telephone calls but do not think it as good as in-person visits.⁶³ This implies that videoconferencing should perhaps not replace in-person visits but be considered as optional or complementary service when in-person visits are not possible.

Finally, in order to understand the context of the study, it is necessary to introduce some specific features of the Finnish eldercare landscape. Finland has a relatively wide age-spatial distribution. This means that many old people live in remote municipalities or in municipalities close to towns or large cities. Simultaneously, the counties responsible are struggling with how to respond to the increasing need for eldercare services with deficit budgets.⁶⁴ The largest occupational groups in the Finnish eldercare system in 2018 were practical nurses and assistive staff, who together made up more than 80% of the eldercare workforce.⁶⁵

In Finland, the use of digital technology has become more common in both home care and residential care over the last years.^64,65 In 2018, telehealth was used in 40% of home care units in the public sector and in 10% of home care units in the private sector.⁶⁶ The COVID-19 pandemic slightly increased the use of digital tools in Finnish health and social services, including videoconferencing.¹⁹ It also helped care workers to overcome their reluctance to use telehealth in their work. For example, among home care workers, the use of videoconferencing made their work more meaningful in the context of social distancing rules, as it allowed care workers to maintain and deepen the care relationship with their clients.⁶⁷

Data and methods

Data

Our analysis is based on the second wave (2021) of the University of Jyväskylä's panel survey on eldercare work, collected from four large trade unions in Finland. The aim of the nationwide survey was to collect information on the working conditions and use of technology among eldercare workers in Finland. Respondents for the first wave of survey data (n = 6903) were collected from the members of four trade unions (SuPer, Tehy, JHL and Talentia) in April 2019. Besides the trade union membership, the inclusion criteria involved that the respondent had to be engaged in ‘daily health care, social care, rehabilitation, or social work with elder or otherwise participate in producing, developing, or managing services for older people’. If not fulfilling these criteria, respondents were excluded from answering the online questionnaire. In the Finnish context, such services are provided by both the social welfare and health care sectors, and eldercare workers can be both social welfare and health care professionals. The number of individuals in the target group varied. For SuPer, the survey was sent using random sampling to every other member of the target group of 38,000. The final sample size was 18,106 respondents with valid email addresses. For Tehy, sampling consisted of two different samples: (1) those among the target group of the survey based on employer information (responses 1,760, sample 7859) and (2) random sampling by including every third member in the member register (responses 666, sample 9600). The sample size was ultimately 17,459. For JHL, the survey was sent to every other member of the target group of 11,000. The final sample size was 4768 respondents with valid email addresses. For Talentia, the members in the potential target group were defined using their titles and education level (8390), all of which were sent the survey. Due to missing or inactive email addresses, the final sample size was 7521 respondents. Of the 6903 responses to the survey, those outside the target group or who declined to answer for other reasons were omitted (528), resulting in the final sample of 6375 responses. Of these, 3607 agreed to participate in further data collection rounds.

The data was collected with an online survey using the 1ka (https://www.1ka.si/d/en) application. The second round of data was collected in April-May 2021 (n = 1679). The questionnaire was administered only to the respondents who agreed to participate in the follow-up surveys in the first round. According to drop-out analysis, there was no notable selection of respondents for the second data collection round. The questionnaire did not include any copyright questions. Most of the questions were taken from public surveys, such as the Quality of Work Life (Statistics Finland) and Eurobarometer surveys. Some questions were drawn from previous Nordic and Finnish academic surveys, in a few cases slightly adapted to the purpose of this study. The questionnaire also included items developed specifically for this longitudinal study.

Measures

Following reviewed literature, we focus our analysis on the relationship between respondent age, education, perceived ICT skills, interest in technology and ICT support to the perceived benefits and drawbacks of videoconferencing tools. Age in years (1) was measured as a continuous variable and (2) education was separated into three levels: (i) primary, (ii) secondary and (iii) tertiary. Perception of one's own ICT skills (3) was measured with the question ‘Does the inadequacy of your own IT or ICT skills slow down the performance of your work tasks?’ with response categories: (i) not at all, (ii) somewhat, (iii) quite a lot and (iv) a lot. Interest in technology (4) was measured with the question ‘How interested you are in technology and its development?’ with response categories: (i) not at all interested, (ii) somewhat interested and (iii) very interested. Organizational ICT support (5) was measured with the question ‘Do you receive support for the use of information technology, information systems, devices or applications related to your work?’ with response categories: (i) way too little, (ii) somewhat too little, (iii) almost enough and (iv) enough. The measures of perceptions on videoconferencing tools (6) include four variables: (i) benefits related to one's own work, (ii) benefits related to clients, (iii) drawbacks related to one's own work and (iv) drawbacks related to clients. The response categories for these variables were (i) not at all, (ii) a little, (iii) somewhat and (iv) a lot. In addition, we use (7) occupation (practical nurses (vocational education), nurses (bachelor level education), other), (8) supervisory status (yes or no), (9) employment sector (public, private, non-governmental organization [NGO]) and (10) workplace type (24 h service housing, home care, institutional care, other) as background variables of work situations for analysis of differences in the experience of using video conference tools.

We construct a three-category variable measuring the experience of using videoconferencing tools in work. The categories were (a) uses videoconferencing tools in direct care work (n = 220), (b), by which we mean use where the care worker is in direct contact with the client via videoconferencing tool, uses videoconferencing tools in work but not in direct care (n = 778), by which we mean other work uses of the videoconferencing, such as work meetings and (c) does not use videoconferencing tools in work (n = 449). In the 2021 survey, respondents who reported using either remote connection devices (e.g., smartphones, tablet computers or home care videophones) or remote care applications (e.g., Zoom, Teams or Skype) were asked whether they used these videoconferencing tools in direct care work. The use of remote connection devices was much more common than the use of remote care equipment (67% and 9%, respectively). In the typology, those who reported using remote connection devices or remote care equipment but not in direct care work were labelled as a ‘semi user’ of videoconferencing tools. This semi-user group mostly consisted of those who used only remote connection devices (92%). By contrast, almost a third of those who used such devices in direct care work reported using both remote connection devices and remote care equipment. Nonetheless, the majority (66%) of this group used only remote connection devices.

Statistics

First, we use a multinomial logistic regression to model the grouping of respondents with regards to their experience of using video conference tools in their work. The selection analysis was conducted with Stata 16.1. Second, we use a path analysis to estimate the effects of ICT support, ICT skills and interest in technology on the perceived benefits and drawbacks of videoconferencing tools in eldercare. We also estimate the indirect and direct effects of age and education on these perceptions. In addition, we employ multi-group analysis to test differences in the overall model depending on the level of experience with videoconferencing tools. For path analyses, we used Mplus 8.4. For estimation, we used the WLSMV-estimator as all variables except for age are defined on an ordinal level. Our models do not include any latent variables. The polychoric correlations between four items measuring benefits and drawbacks clearly indicated a two-factor model in which measures of benefits and drawbacks load to their individual factor. However, with only two indicators for each latent construct, the model was not identifiable. Thus, we decided to form sum scores for the two dimensions and include them as observed ordinal variables in our model. The Cronbach's alpha for benefits and drawbacks was estimated at 0.69 and 0.72, respectively. We used standardized coefficients, which provide a uniform scale for effects and are thus suitable for our analytical purposes. With the WSMLV-estimator, the direct effects of endogenous variables can be interpreted as a normal regression coefficient (standardized in our case). The WLSMV-estimator estimates a continuous latent variable behind the ordinal observed variables.^68,69

Results

Table 1 presents descriptive statistics of the analytical variables by videoconferencing use. The table shows that videoconferencing users and semi-users are typically younger, better educated, work as nurses and are in supervisory positions compared to non-users. Moreover, users are more often employed in the private sector, do home care and are more interested in technology in general. To consider the dependence between these variables, we also examined the grouping of respondents into different videoconferencing user experience groups using multinomial logistic regression. Figure 1 presents average marginal effects¹ derived from the multinomial logistic model predicting the experience of using videoconferencing tools in care work with different background factors.

Figure 1.

Average marginal effects and 95% CI from the multinomial logistic regression model predicting experience of using videoconferencing tools in care work (n = 1373). Note. User: uses videoconferencing tools in direct care work; Semi-user: uses videoconferencing tools in work, but not in direct care work; Non-user (ref.): does not use videoconferencing tools in work.

Table 1.

Descriptive statistics (%) of the use of videoconferencing tools.

		Use of videoconferencing tools			N	Difference test
		User (n = 220)	Semi-user (n = 778)	Non-user (n = 449)	N	Difference test
Gender	Female	98	97	96	1392	χ²(2) = 1.36, p = 0.51
Gender	Male	2	3	4	49	χ²(2) = 1.36, p = 0.51
Age	18–29	9	7	14	136	χ²(8) =55.91, p < 0.001
	30–39	16	18	19	260
	40–49	28	29	20	380
	50–59	41	38	31	524
	60 or more	5	7	16	139
Education	Primary	1	1	2	17	χ²(4) =55.63, p < 0.001
	Secondary	68	66	84	1040
	Tertiary	31	33	14	388
Occupation	Practical nurse	65	59	83	973	χ²(4) =78.92, p < 0.001
	Nurse	15	22	11	253
	Other	20	19	6	220
Supervisor	No	77	84	96	1256	χ²(2) = 56.15, p < 0.001
Supervisor	Yes	23	15	4	187	χ²(2) = 56.15, p < 0.001
Sector	Public	68	81	56	993	χ²(4) = 79.13, p < 0.001
	Private	28	17	39	349
	NGO	5	3	5	50
Workplace	24 h service housing	44	38	62	671	χ²(6) = 98.06, p < 0.001
	Home care	34	26	16	352
	Institutional care	5	14	14	182
	Other	17	21	8	240
Low ICT skills slow down work tasks	Not at all	40	41	46	613	χ²(6) = 6.08, p = 0.41
	Somewhat	54	53	47	739
	Quite a lot	4	5	5	72
	A lot	2	1	2	19
Interested in technology	Not at all	8	10	17		χ²(4) = 24.01, p < 0.001
	Somewhat	60	66	65
	Very	31	23	18
Received ICT support	Way too little	23	22	22	305	χ²(6) = 12.91, p = 0.04
	Somewhat too little	29	32	23	409
	Almost enough	29	30	36	442
	Almost enough	19	16	19	243

Statistically significant differences were found between the three user groups in all factors except gender. Most of these differences occurred between semi-user and non-user groups. Middle aged workers (30–59) had a higher probability of belonging to the semi-user group than the non-user group than the youngest age group. Nurses and those working in other occupations clearly had a higher probability of belonging to the semi-user group than the non-user group when compared to practical nurses. Care workers with subordinates had a higher probability of using video conference tools in direct care work and other work duties than not using them at all. Those working in the private sector or NGOs had a lower probability of being in the semi-user group than the non-user group. There was no statistically significant difference for the user group. Those working in home care were more likely than those working in 24 h service housing to use videoconferencing tools in indirect care work than not using them. In contrast, those working in institutional care had a lower probability than those in 24 h service housing to use videoconferencing tools in indirect care work than not using them at all. In addition, employees in institutional care or other workplaces had a higher probability of being in the semi-user group than the non-user group. Thus, only supervisory status and working in home care (and institutional care, negatively) predicted the use of videoconferencing tools in direct care work.

We also conducted selection analyses separately for both survey years, as a robustness test and to address the role of the COVID-19 pandemic on the use of these devices. The 2019 wave took place before COVID-19, and the 2021 wave collected during COVID-19. Due to data limitations, we were only able to use a binary indicator, that is, whether the respondent used remote connection devices or remote care equipment (the ‘semi-user’ group) in the given year. According to the results (Appendix 1), work and background characteristics predicted similarly the use of videoconferencing tools before and during the COVID-19 pandemic. The most notable difference was that the age of the respondents was not related to the use of videoconferencing tools before the pandemic but did have a statistically significant effect during it (30–59-year-olds used them more often than younger respondents). Thus, there seems to be no difference in the selection between non-user and semi-user groups before and during the pandemic. However, we cannot rule out the possibility of the process of using video conference tools in direct care work changing during our observation period.

Next, we examined whether there were differences in the level of perceived benefits and drawbacks of videoconferencing tools based on the respondents’ experiences of using them. According to the results presented in Figure 2, there were statistically significant differences between user groups for all four variables measuring benefits and drawbacks, especially between the semi-user and user groups. Users perceive more benefits in the use of videoconferencing tools – both for clients and their own work – than semi-users and non-users. Users also perceived more drawbacks, both for clients and their own work, than semi-users and non-users.

Figure 2.

The level of perceived benefits and drawbacks of videoconferencing tools by user experience. Note. User: uses videoconferencing tools in direct care work; Semi-user: uses videoconferencing tools in work, but not in direct care work; Non-user: does not use videoconferencing tools in work.

In the next phase, we conducted a path analysis for the overall sample predicting perceptions of the benefits and drawbacks of using videoconferencing tools in direct care work. As Figure 3 and the associated statistics show, the hypothesized model fits the data well, although some of the expected effects were not statistically significant.

Figure 3.

Results (standardized coefficients) from path analysis (WLSMV) predicting the perceived benefits and drawbacks of videoconferencing tools in eldercare work (RMSEA = .040, CFI = .99, TLI = .91, SRMR = .013). n = 1576; * p < 0.05; ** p < 0.01; *** p < 0.00.

Age had a negative effect on education and a positive effect on lack of ICT skills. Thus, older respondents, on average, had a lower level of education and ICT skills than younger ones. In addition, age had a moderate negative effect on perceived ICT support, contrary to what was expected. Older respondents might need more support with ICT than younger ones and thus, the current level of support was perceived as inadequate. The negative effect on interest in technology was not statistically significant.

Figure 3 also shows that education level had a positive effect on interest in technology and perceived ICT support. The direction of this effect was that more support would be provided for those with a lower level of education. The reasons for this could be explained similarly to the effect found for age: the respondents with lower education levels might require more support than they receive to feel that it is adequate. The perceived ICT support, in turn, had a negative effect on respondents’ lack of ICT skills. The effect of support on the perceived benefits of videoconferencing tools was small but statistically significant. Similarly, there was a small negative statistically non-significant effect on the perceived drawbacks of videoconferencing tools. Lack of ICT skills had a positive effect on both the perceived benefits and drawbacks of videoconferencing tools after controlling for all other variables. Thus, the lower the level of ICT skills, the more benefits and drawbacks the respondents perceived in using videoconferencing tools. Additionally, interest in technology had a positive effect on the perceived benefits of videoconferencing tools, that is, the more interested the respondents were in technology overall, the more benefits they ascribe to the use of videoconferencing tools. In addition, education level had a positive effect on the perceived benefits of videoconferencing tools. A small but statistically significant negative correlation was found between the perceived benefits and drawbacks of videoconferencing tools.

In addition to the analysis presented in Figure 3, we estimated the indirect effects of age, education and ICT support through multiple mediators on perceived benefits and drawbacks. There were statistically significant total indirect effects of education (Beta = .07, p < .001) and ICT support (Beta = −.03, p = .001) on benefits. Both coefficients were smaller than the direct effects shown in Figure 3. Almost all of the indirect effect of education comes from its effect via interest in technology (Beta = .06, p < .001). ICT support had only one mediator, namely lack of ICT skills. For age, the total indirect effect was not statistically significant. Age (Beta = .05, p < .001) and ICT support (Beta = −.03, p = .003) had statistically significant total indirect effects on perceived drawbacks. For ICT support, the indirect coefficient was smaller than the direct effect. Most of the indirect effect of age comes from its effect via lack of ICT skills (Beta = .04, p = .002). Education did not have a statistically significant total indirect effect on perceived drawbacks. There were also some more complex and statistically significant mediations, but their coefficients were very small.

In the next phase, we estimated a multi-group path model using respondent experience of videoconferencing tools as grouping factor. The three groups were: those who use videoconferencing tools in direct care work (‘users’, n = 219), those who use videoconferencing tools in work, but not in direct care work (‘semi-users’, n = 774) and those who do not use videoconferencing tools in work (‘non-users’, n = 446). We used a Wald test to test for group differences in coefficients. With multi-group analysis, the aim is to estimate whether the hypothesized model differs between those who have actual experience of videoconferencing tools and those who do not. Thus, we examine the difference between expectations and experiences regarding videoconferencing tools.

As shown in Appendix 2, there were some differences in coefficients between these three groups. First, interest in technology had a statistically significant negative effect on perceived drawbacks only for those who have actual experience of using these tools in direct care work (users). By contrast, interest in technology had a statistically significant positive effect on perceived benefits only for those who have some experience of using video conference tools in their work (semi-users). Similarly, education had positive associations with interest in technology and ICT support for those who have some experience of using videoconference tools in their work (semi-users). In other words, there were clear differences in how organization-level and individual-level characteristics were related to the actual versus expected benefits or drawbacks of videoconferencing tools.

Discussion

Key findings

Firstly, the results confirm that older respondents, on average, have a lower education level and lower ICT skills.^38,41–44 They also perceive current ICT support to be inadequate. The respondents with higher education levels were, on average, more interested in technology and were more satisfied with current ICT support,⁴² as also found in previous research. The respondents with more interest in technology and higher education levels perceived more benefits in using videoconferencing tools in eldercare, and this is also in line with previous research.^38,42,49 Unexpectedly, a lack of ICT skills had a positive effect on both perceived benefits and drawbacks of videoconferencing tools after controlling for all other variables. The results also show the indirect effects of age, education and ICT support through multiple mediators. Hence, the effects of age and education on the perception of video conference tools are apparently partly mediated by ICT support, the lack of ICT skills and interest in technology.

Second, the group differences between users, semi-users and non-users of videoconferencing tools in eldercare indicate that there are some differences in coefficients between the three groups. For example, interest in technology has a significant positive effect on perceived benefits for those who had used video conference tools in their work at least to some extent. It is then clear that the actual experience of using technology in a work context is significant when considering users’ perceptions of the benefits of technology.

Third, the results point out that eldercare workers who use videoconferencing in direct care work see it as much more beneficial, both for their own work and for clients, than those who use it in other types of work. In practice, this means that the use of videoconferencing tools is seen as much more useful for contacting clients as in, for example, online work meetings or medical training for eldercare staff. This outcome is associated with previous findings on the topic of eldercare workers’ perceptions of their work conditions. Eldercare workers’ feelings of increased workload, constant time pressure and lack of possibility to complete their work properly are widely recognized,^9–11 as are the increases in time used for work-related staff meetings¹² and other duties that are not actual care work.¹³ Our findings indicate that eldercare workers recognize the value of videoconferencing especially when it can be used for the core function of care work.

Theoretical implications

As, in our research setting, the use of videoconferencing in eldercare was decided at the organizational level, it was not relevant to examine the acceptance of using the TAM and its extensions.^24–26 The use of the original TAM model within health informatics has proven to be difficult, and often the model has been extended to fit the dynamic field of health and social services by integrating components and variables in specific contextual settings.^34–38 This applied to our study as well. We selected users’ perceptions of the benefits of videoconferencing technologies as a core component of our theoretical model because, in an environment where the use of technology has already been decided by the organization, it is central for functional care work.¹⁶ Based on our results, the tasks which videoconferencing tools are used for are highly related to their perceived benefits. Thus, our study demonstrates that it is not enough to include only technology user, cultural context and a certain technology in the theoretical model, as also noted in previous studies,^36,37,39,40 but also how and for what purpose technology is used in organizations should be considered.

Our study shows that several issues still require further investigation. First, our analysis which grouped respondents into different videoconferencing user experience groups revealed that supervisory status predicts the use of videoconferencing tools in direct care work. However, we can only hypothesize the reasons behind this finding. For example, those with subordinates might have more control over their work and have wider care work experience. However, future studies should look to explain why supervisory status leads to a higher probability of belonging to the ‘user’ group.

Practical implications

Our analysis provides several practical implications, especially for organizations considering implementing, or already using, videoconferencing tools in eldercare. First, it is notable that actual experience of videoconferencing tools in direct care work correlates with a more positive perception of this technology related to one's own work but with a more negative perception related to clients. In this regard, it might be beneficial for healthcare and social welfare organizations to provide short-term trial periods for care workers to test videoconferencing tools in direct care work situations before making the decision to implement these technologies. It is worth noting that this study only offers information from a care worker's perspective, but complementary data is required to investigate the client perspective. Based on our data, we also concluded that more ICT support should probably be targeted to those with a lower level of education and higher age. Based on our path analysis for the overall sample, eldercare workers seemingly did not feel that the ICT support provided was adequate. Therefore, further ICT support could increase care worker confidence before initiating a video session.

Our study revealed that those with subordinates are more often users of videoconferencing tools in direct care work and they see the use of this technology in a more positive way. We assume that this could be because they have more influence over their own work. Thus, they can use videoconferencing tools in more flexible and individual ways than those who work as subordinates. Alternatively, those in managerial positions may have a higher level of education, which has a positive effect on interest in technology. Previous literature detailed that, in eldercare, one of the biggest reasons for moral stress is the lack of influence over one's own work¹⁴ and therefore, we assume that voluntary use of technology may lead to more beneficial perceptions of it. Considering clients’ individual needs and wishes is concordant with care worker ethical codes^16,60,61 and therefore, it could be desirable to choose either in-place care or care through videoconferencing on occasion. Furthermore, future studies should examine how flexibility and individuality when using videoconferencing tools in eldercare affects the perceived benefits of these technologies.

Limitations

As with other empirical studies, our study also has limitations. Firstly, we collected data only in Finland (where we had no access to non-union members) and our results do not apply to all social and cultural conditions and further studies are required to address other cultural and societal contexts. Secondly, given the extremely high proportion of females in the target population, we cannot adequately address gender effects. Thirdly, Care for older people is divided into institutional care and home care and their variations. These care settings are diverse and the use of videoconferencing tools in direct care is not as relevant in institutional care, where distance is not a barrier, as it may be in home care. Fourthly, as precise data on the degree of unionization of eldercare workers in Finland are not available, any generalization of these results to all Finnish eldercare workers would be speculative. Future research needs to consider the varying institutional settings when investigating videoconferencing tools in care work.

Conclusions

Our study provides information on how eldercare workers perceive the benefits and drawbacks of videoconferencing tools in their work. Considering RQ1 and RQ3, we found that the personal characteristics of care workers are associated with both the use of videoconferencing tools and the perception of the benefits and drawbacks of them. Regarding RQ2, we discovered that prior use of videoconferencing tools, especially in direct care work, affects the perceived benefits of them. Based on these results, we suggest that healthcare and social welfare organizations pay attention to the characteristics of care workers, especially when estimating the necessary amount of ICT support. Organizations should also realize that care workers’ concrete experiences of using videoconferencing tools in direct care work increase their perception of both the benefits and drawbacks of these tools.

Footnotes

Acknowledgements

The authors would like to give special thanks to the respondents who participated in the University of Jyväskylä's survey on eldercare work in 2021. The authors also want to give our thanks to all members of the Centre of Excellence in Research on Age and Care for their feedback and comments on this study.

Contributorship

All authors participated in designing and conceptualization of the study. VM directed the implementation of the study, led all aspects of the work and drafted the article. All authors contributed to the acquisition of data. TO conducted data analysis. VM interpreted the findings. All authors contributed to reviewing the article and revising it critically. All authors approved the manuscript's submission for publication.

Consent to participate

Electronic informed consent was mandatory to all participants. No human-identifiable data is reported in this study. The participants in this study were care workers, and the focus of this study is on their perceptions of the benefits and drawbacks of using videoconferencing tools in the care of older people. Patient data was not used in this study. In accordance with ICMJE guidelines, we confirm that patient consent was not required.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical approval

According to the instructions of Finnish National Board of Research Integrity, ethical approval is not required in this study.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by the Academy of Finland's Centre of Excellence in Research on Ageing and Care (project no. 312367 and 336671) and the Strategic Research Council at the Academy of Finland (project no. 327145, 352501, 352505 and 327149).

Guarantor

VM.

ORCID iDs

Ville Mustola

Sakari Taipale

Notes

Appendix 1.

Average marginal effects (and 95% CI) from the binary logistic regression model predicting the use videoconferencing tools in work by survey year (2019: n = 8434, 2021: n = 1392).

Appendix 2.

Multi-group path analysis (WLSMV) predicting the perceived benefits and drawbacks of videoconferencing tools in elder care work.

User: uses videoconferencing tools in direct care work (n = 219)

Semi-user: uses videoconferencing tools in work, but not in direct care work (n = 778)

Non-user: does not use videoconferencing tools in work (n = 449)

χ²(33) = 41.11, p = 0.16, RMSEA = 0.023.

References

Buvik

Bergmo

Bugge

, et al. Cost-effectiveness of telemedicine in remote orthopedic consultations: randomized controlled trial. J Med Internet Res 2019; 21: e11330.

La Cour

Højlund

. Untimely welfare technologies. Nord J Work Life Stud 2019; 9: 69–87.

Bergschöld

. When saving time becomes labor: time, work, and technology in homecare. Nord J Work Life Stud 2018; 8: 3–21.

Kamp

Hansen

. Negotiating professional knowledge and responsibility in cross-sectoral telemedicine. Nord J Work Life Stud 2019; 9: 13–32.

Gajarawala

Pelkowski

. Telehealth benefits and barriers. J Nurse Pract 2021; 17: 218–221.

Riedl

. On the stress potential of videoconferencing: definition and root causes of zoom fatigue. Electron Mark 2022; 32: 153–177.

Dalley

Rahman

Ivaldi

. Health care professionals’ and patients’ management of the interactional practices in telemedicine videoconferencing: a conversation analytic and discursive systematic review. Qual Health Res 2021; 31: 804–814.

Ollevier

Aguiar

Palomino

, et al. How can technology support ageing in place in healthy older adults? A systematic review. Public Health Rev 2020; 41: 1–26.

Ruotsalainen

Jantunen

Sinervo

. Which factors are related to Finnish home care workers’ job satisfaction, stress, psychological distress and perceived quality of care? – a mixed method study. BMC Health Serv Res 2020; 20: 896.

10.

Grasmo

Liaset

Redzovic

. Home care workers’ experiences of work conditions related to their occupational health: a qualitative study. BMC Health Serv Res 2021; 21: 962.

11.

Leverton

Burton

Beresford-Dent

, et al. Supporting independence at home for people living with dementia: a qualitative ethnographic study of homecare. Soc Psychiatry Psychiatr Epidemiol 2021; 56: 2323–2336.

12.

Allen

Lehmann-Willenbrock

. The key features of workplace meetings: conceptualizing the why, how, and what of meetings at work. Organ Psychol Rev 2022; 13.

13.

Strandell

. Care workers under pressure – A comparison of the work situation in Swedish home care 2005 and 2015. Health Soc Care Community 2020; 28: 137–147.

14.

Weiste

Paavolainen

Olin

, et al. Elderly care practitioners’ perceptions of moral distress in the work development discussions. Healthcare (Basel) 2023; 11: 291.

15.

Chu

Zewu

, et al. PP-HumanSeg: connectivity-aware portrait segmentation with a large-scale teleconferencing video dataset. In: IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Waikoloa, HI, USA, 2022: pp. 202–209.

16.

Frennert

. Moral distress and ethical decision-making of eldercare professionals involved in digital service transformation, disability and rehabilitation. Assist Technol 2023; 18: 156–165.

17.

Barrutia

Echebarria

. Effect of the COVID-19 pandemic on public managers’ attitudes toward digital transformation. Technol Soc 2021; 67: 101776.

18.

Esber

Teufel

Jahre

, et al. Predictors of patients’ acceptance of video consultation in general practice during the coronavirus disease 2019 pandemic applying the unified theory of acceptance and use of technology model. Digit Health 2023; 9: 1–15.

19.

Häyrinen

. Perspectives on the progress of digitalization in social and health care. Finnish J EHealth EWelfare 2020; 12: 160–161.

20.

Koonin

Hoots

Tsang

, et al. Trends in the use of telehealth during the emergence of the COVID-19 pandemic – United States, January-march 2020. MMWR Morb Mortal Wkly Rep 2020; 69: 1595–1599.

21.

Nearing

Lum

Dang

, et al. National geriatric network rapidly addresses trainee telehealth needs in response to COVID-19. J Am Geriatr Soc 2020; 68: 1907–1912.

22.

Thomas

Haydon

Mehrotra

, et al. Building on the momentum: sustaining telehealth beyond COVID-19. J Telemed Telecare 2022; 28: 301–308.

23.

Kaplan

Harris-Salamone

. Health IT success and failure: recommendations from literature and an AMIA workshop. J Am Med Inform Assoc 2009; 16: 291–299.

24.

Nilsen

Seeing

Ericsson

, et al. Characteristics of successful changes in health care organizations: an interview study with physicians, registered nurses and assistant nurses. BMC Health Serv Res 2020; 20: 147.

25.

Nilsen

Dugstad

Eide

, et al. Exploring resistance to implementation of welfare technology in munincipal healthcare services – a longitudinal case study. BMC Health Serv Res 2016; 16: 657.

26.

Awanto

Ardianto

Prasetya

. UTAUT model implementation on user behavior in use of information technology. Jurnal Teknologi dan Manajemen Informatika 2020; 6: 53–59.

27.

Brewster

Mountain

Wessels

, et al. Factors affecting frontline staff acceptance of telehealth technologies: a mixed-method systematic review. J Adv Nurs 2014; 70: 21–33.

28.

Ebad

. Healthcare software design and implementation – A project failure case. Softw Pract Exp 2020; 50: 1258–1276.

29.

Ariza-Vega

Prieto-Moreno

Mora-Traverso

, et al. Co-creation of mHealth intervention for older adults with hip fracture and family caregivers: a qualitative study. Disabil Rehabil Assist Technol 2022: 1–10. ahead-of-print(ahead-of-print).

30.

Fisk

Rogers

Charness

, et al. Designing for older adults: Principles and creative human factors approaches. 2nd ed. Boca Raton: CRC Press, 2009.

31.

Gomes

Romao

. The success of IS/IT projects in the healthcare sector: stakeholders’ perceptions. In: 10th Iberian Conference on Information Systems and Technologies (CISTI), Aveiro, Portugal, 2015, pp. 1–7.

32.

Mohd Adzmi

Hassan

. A theoretical framework of critical success factors on information technology project management during project planning. Int J Eng Technol 2018; 7: 650–655.

33.

van Gemert-Pijnen

JEWC

Nijland

van Limburg

, et al. A holistic framework to improve the uptake and impact of health technologies. J Med Internet Res 2011; 13: e111.

34.

Ladan

Wharrad

Windle

. Towards understanding healthcare professionals’ adoption and use of technologies in clinical practice: using Qmethodology and models of technology acceptance. J Innov Health Inform 2018; 25: 27–37.

35.

Rahimi

Nadri

Lotfnezhad Afshar

, et al. A systematic review of the technology acceptance model in health informatics. Appl Clin Inform 2018; 9: 604–634.

36.

Momani

. The unified theory of acceptance and use of technology: a new approach in technology acceptance. Int J Sociotechnol Knowl Dev 2020; 12: 79–98.

37.

Bagozzi

. The legacy of the technology acceptance model and a proposal for a paradigma shift. J Assoc Inf Syst 2007; 8: 244–254.

38.

Venkatesh

Morris

Davis

, et al. User acceptance of information technology: toward a unified view. MIS Q 2003; 27: 425–478.

39.

Nieboer

Van Hoof

Van Hout

, et al. Professional values, technology and future health care: the view of health care professionals in The Netherlands. Technol Soc 2014; 39: 10–17.

40.

Rajak

Shaw

. An extension of technology acceptance model for mHealth user adoption. Technol Soc 2021; 67: 101800.

41.

Desjardins

Ederer

. Socio-demographic and practice-oriented factors related to proficiency in problem solving: a lifelong learning perspective. Int J Lifelong Educ 2015; 34: 468–486.

42.

Graham

. Group differences in attitudes towards technology among Americans. New Media Soc 2010; 12: 985–1003.

43.

Kubiatko

. The comparison of different age groups on the attitudes toward and the use of ICT. Educ Sci Theory Pract 2013; 13: 1263–1272.

44.

Wicht

Reder

Lechner

. Sources of individual differences in adults’ ICT skills: a large-scale empirical test of a new guiding framework. PLoS One 2021; 16: e0249574.

45.

Eger

Klement

Tomczyk

, et al. Different user groups of university students and their ICT competence: evidence from three countries in central Europe. J Balt Sci Educ 2018; 17: 851–866.

46.

Hämäläinen

De Wever

Malin

, et al. Education and working life: VET adults’ problem-solving skills in technology-rich environments. Comput Educ 2015; 88: 38–47.

47.

Hämäläinen

De Wever

Nissinen

, et al. Understanding adults’ strong problem-solving skills based on PIAAC. J Workplace Learn 2017; 29: 537–553.

48.

Gagnon

Desmartis

Labrecque

, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst 2012; 36: 241–277.

49.

. ICT self-efficacy and ICT interest mediate the gender differences in digital reading: a multilevel serial mediation analysis. Int J Emerg Technol Learn 2022; 17: 211–225.

50.

Chen

. ICT-related behavioral factors mediate the relationship between adolescents’ ICT interest and their ICT self-efficacy: evidence from 30 countries. Comput Educ 2020; 159: 104004.

51.

Newbould

Mountain

Hawley

, et al. Videoconferencing for health care provision for older adults in care homes: a review of the research evidence. Int J Telemed Appl 2017: 5785613–7.

52.

Newbould

Ariss

Mountain

, et al. Exploring factors that affect the uptake and sustainability of videoconferencing for healthcare provision for older adults in care homes: a realist evaluation. BMC Med Inform Decis Mak 2021; 21: 13.

53.

Daly

Depp

Graham

, et al. Impacts of the stay-at-home order on community-dwelling older adults and how technologies may help: focus group study. JMIR Aging 2021; 4: e25779.

54.

Husebø

AML

Storm

. Virtual visits in home health care for older adults. ScientificWorldJournal 2014: 689873–11.

55.

Wakefield

Buresh

Flanagan

, et al. Interactive video specialty consultations in long-term care. J American Geriatr Soc 2004; 52: 789–793.

56.

Van den Broek

. Perforated body work: the case of tele-nursing. Work Employ Soc 2017; 31: 904–920.

57.

Sävenstedt

Zingmark

Hydén

, et al. Establishing joint attention in remote talks with the elderly about health: a study of nurses’ conversation with elderly persons in teleconsultations. Scand J Caring Sci 2005; 19: 317–324.

58.

Kapadia

Ariani

, et al. Emerging ICT implementation issues in aged care. Int J Med Inform 2015; 84: 892–900.

59.

Mair

Goldstein

May

, et al. Patient and provider perspectives on home telecare: preliminary results from a randomized controlled trial. J Telemed Telecare 2005; 11: 95–97.

60.

Sairaanhoitajan eettiset ohjeet. Suomen Sairaanhoitajat ry, https://www.slideshare.net/THLfi/kotihoidon-ja-ymprivuorokautisen-hoidon- toimintatavat-2018-118997643. (2021, accessed 6 April 2023).

61.

Lähihoitajan eettiset ohjeet. Suomen lähi- ja perushoitajaliitto Super ry, https://www.superliitto.fi/site/assets/files /4599/superliitto-lahihoitajan-eettiset-ohjeet-2019.pdf (2019, accessed 6 April 2023).

62.

Mao

Tam

, et al. Barriers to telemedicine video visits for older adults in independent living facilities: mixed methods cross-sectional needs assessment. JMIR Aging 2022; 5: e34326.

63.

Paul

Salmon

Sinnarajah

, et al. Web-based videoconferencing for rural palliative care consultation with elderly patients at home. Support Care Cancer 2019; 27: 3321–3330.

64.

EPTA report. Technologies in Care for Older People, https://eptanetwork.org/images/documents/minutes /EPTA_report_2019.pdf (2019, accessed 7 April 2023).

65.

THL. Kotihoidon ja ympärivuorokautisen hoidon toimintatavat 2018. Vanhuspalvelujen tila 2018-tutkimusaineisto. Helsinki: Terveyden ja hyvinvoinnin laitos, https://www.slideshare.net/THLfi/kotihoidon-ja- ymprivuorokautisen-hoidon-toimintatavat-2018-118997643 (2018, accessed 29 December 2022).

66.

THL. Terveys- ja sosiaalipalveluiden henkilöstö 2018: Tuleva sote-uudistus koskee 7,3 prosenttia työssäkäyvistä. Tilastoraportti 47/2021. Helsinki: Terveyden ja hyvinvoinnin laitos, https://urn.fi/URN:NBN:fi-fe2021121460373 (2021, accessed 29 December 2023).

67.

Kuoppakangas

Lindfors

Stenwall

, et al. COVID-19 triggering homecare professionals’ change of attitudes towards e-welfare. Finnish J EHealth EWelfare 2020; 12: 241–249.

68.

Muthén

. Mplus: Statistical analysis with latent variables: User’s guide (version 8). Los Angeles, CA: Authors, 2017.

69.

Hoyle

. Handbook of structural equation modeling. New York: Guilford Press, 2012.

Benefits and drawbacks of videoconferencing in eldercare from care workers’ perspectives

Abstract

Objective

Methods

Results

Conclusions

Keywords

Introduction

Data and methods

Data

Measures

Statistics

Results

Discussion

Key findings

Theoretical implications

Practical implications

Limitations

Conclusions

Footnotes

Acknowledgements

Contributorship

Consent to participate

Declaration of conflicting interests

Ethical approval

Funding

Guarantor

ORCID iDs

Notes

Appendix 1.

Appendix 2.

References