Change agent's role in facilitating use of technology in residential aged care: A systematic review.

OBJECTIVE: To synthesise the evidence on the roles and outcomes of change agents in facilitating the use of powered technology systems and devices for staff end-users in residential aged care workplaces. DESIGN: Systematic review and narrative synthesis. DATA SOURCES: CINAHL, MEDLINE and EMBASE databases were searched for articles published in English between January 2010 and July 2022. REVIEW METHODS: Two of three reviewers independently screened each title and abstract, and subsequently the full texts of selected records. The Mixed Method Appraisal Tool was used to assess the quality of the included articles. RESULTS: Of 3,680 records identified, ten articles reporting nine studies were included. In all the studies, the change agent role was a minor component embedded within implementation processes. Three key change agent roles were identified: 1) providing guidance, expertise, and support with implementing a new technology; 2) delivering training to others, and 3) troubleshooting and responding to issues. The key outcome of change agent roles related to achieving project implementation milestones and higher levels of implementation of technology. Change agent processes, however, were compromised when the designated change agent role was included late in the implementation process, or was not supported, recognised, embraced, or when roles or responsibilities were unclear. The direct contribution of change agents was difficult to elucidate because the roles and outcomes of change agents were embedded in multi-faceted implementation strategies. CONCLUSIONS: The change agent can play an important role in facilitating technology implementation by providing support, training, and troubleshooting. Challenges with the change agent role included unclear role expectations and appointment late in the implementation process. Overall, there was limited evidence specific to the role and outcome of the change agent role to inform ideal approaches for their role in technology facilitation for end-users in residential aged care workplaces.

A Psychological Investigation of Selection Criteria for Learning Agents (Super Users) and Allocation of Responsibilities in the Implementation of Technological Change.

Purpose: In the introduction of new technologies into organizations, there has been an increasing trend to recruit and make use of the so-called "super users" to help ensure the future use of the technology in question. Little is known about the criteria that should ideally be considered in the selection of these super users, or about the best way to carve up the roles and responsibilities in this process between super users and middle management. In this study, we investigated (1) which criteria should be emphasized in the selection of super users, and (2) how middle management and super users understand and negotiate the responsibilities of their respective roles during implementation of technological change. Methods: We conducted 10 individual semi-structured interviews and used thematic analysis of this data set to identify selection criteria, roles, and responsibilities. Results: We found that the main selection criteria for super users should be: (1) availability and local knowledge, (2) technological skills, (3) pedagogical skills, and (4) proactiveness. The main roles and responsibilities that should be carved up between management and super users can be grouped into two overarching categories, each with several subcategories. Within the Learning culture category, the responsibilities are to (1) facilitate collective learning, (2) engage with criticism, and (3) promote collective sharing; and within the Individual learning category, to (4) facilitate individual learning, (5) provide instrumental support, and (6) provide emotional support. Discussion and Conclusion: Based on the findings, we propose a conceptual model of technological implementation and the construction of a culture of organizational learning, entitled ECo-System Of Learning in Organizations (ECSO-Learn); we additionally show how a learning agent (previously known as a super user) can be recruited to best fit into this model of long-term organizational learning.

Community Structure of a Mental Health Internet Support Group: Modularity in User Thread Participation.

BACKGROUND: Little is known about the community structure of mental health Internet support groups, quantitatively. A greater understanding of the factors, which lead to user interaction, is needed to explain the design information of these services and future research concerning their utility. OBJECTIVE: A study was conducted to determine the characteristics of users associated with the subgroup community structure of an Internet support group for mental health issues. METHODS: A social network analysis of the Internet support group BlueBoard (blueboard.anu.edu.au) was performed to determine the modularity of the community using the Louvain method. Demographic characteristics age, gender, residential location, type of user (consumer, carer, or other), registration date, and posting frequency in subforums (depression, generalized anxiety, social anxiety, panic disorder, bipolar disorder, obsessive compulsive disorder, borderline personality disorder, eating disorders, carers, general (eg, "chit chat"), and suggestions box) of the BlueBoard users were assessed as potential predictors of the resulting subgroup structure. RESULTS: The analysis of modularity identified five main subgroups in the BlueBoard community. Registration date was found to be the largest contributor to the modularity outcome as observed by multinomial logistic regression. The addition of this variable to the final model containing all other factors improved its classification accuracy by 46.3%, that is, from 37.9% to 84.2%. Further investigation of this variable revealed that the most active and central users registered significantly earlier than the median registration time in each group. CONCLUSIONS: The five subgroups resembled five generations of BlueBoard in distinct eras that transcended discussion about different mental health issues. This finding may be due to the activity of highly engaged and central users who communicate with many other users. Future research should seek to determine the generalizability of this finding and investigate the role that highly active and central users may play in the formation of this phenomenon.

Quality improvement teams, super-users, and nurse champions: a recipe for meaningful use?

OBJECTIVE: This study assessed whether having an electronic health record (EHR) super-user, nurse champion for meaningful use (MU), and quality improvement (QI) team leading MU implementation is positively associated with MU Stage 1 demonstration. METHODS: Data on MU demonstration of 596 providers in 37 ambulatory care clinics came from the clinical data warehouse and administrative systems of UNC Health Care. We surveyed the 37 clinics about champions, super-users, and QI teams. We used generalized estimating equation methods with an independence working correlation matrix to account for clustering within clinics and to weight contributions from each clinic according to clinic size. RESULTS: Having a QI team lead MU implementation was significantly associated with MU demonstration (odds ratio, OR = 3.57, 95% CI, 1.83-6.96, P < .001, Table 2). Having neither a nurse champion nor an EHR super-user was significant. CONCLUSION: Our findings support the alignment of MU with QI efforts by having the QI team lead MU implementation.