A quantitative approach to social network analysis involves the application of mathematical and statistical techniques and graphical presentation of results. Nonetheless—as with all ...sciences—subjectivity is an integral aspect of network analysis, manifested in the selection of measures to describe connection patterns and actors’ positions (e.g., choosing a centrality indicator), in the visualization of social structure in graphs, and in translating numbers into words (telling the story). Here, we use network research as an example to illustrate how quantitative and qualitative approaches, techniques, and data are mixed along a continuum of fusion between quantitative and qualitative realms.
Workforce development is an important aspect of evidence-informed decision making (EIDM) interventions. The structure of formal and informal social networks can influence, and be influenced, by the ...implementation of EIDM interventions.
In a mixed methods study we assessed the outcomes of a targeted training intervention to promote EIDM among the staff in three public health units in Ontario, Canada. This report focuses on the qualitative phase of the study in which key staff were interviewed about the process of engagement in the intervention, communications during the intervention, and social consequences.
Senior managers identified staff to take part in the intervention. Engagement was a top-down process determined by the way organizational leaders promoted EIDM and the relevance of staff's jobs to EIDM. Communication among staff participating in the workshops and ongoing progress meetings was influential in overcoming personal and normative barriers to implementing EIDM, and promoted the formation of long-lasting social connections among staff. Organization-wide presentations and meetings facilitated the recognition of expertise that the trained staff gained, including their reputation as experts according to their peers in different divisions.
Selective training and capacity development interventions can result in forming an elite versus ordinary pattern that facilitates the recognition of in-house qualified experts while also strengthening social status inequality. The role of leadership in public health units is pivotal in championing and overseeing the implementation process. Network analysis can guide and inform the design, process, and evaluation of the EIDM training interventions.
Worldwide mandates for social distancing and home-quarantine have contributed to loneliness and social isolation. We conducted a systematic scoping review to identify network-building interventions ...that address loneliness and isolation, describe their components and impact on network structure, and consider their application in the wake of COVID19. We performed forward and backward citation tracking of three seminal publications on network interventions and Bibliographic search of Web of Science and SCOPUS. We developed data charting tables and extracted and synthesized the characteristics of included studies, using an iteratively updating form. From 3390 retrieved titles and abstracts, we included 8 studies. These interventions focused on building networks at either individual- or group-levels. Key elements that were incorporated in the interventions at varying degrees included (a) creating opportunities to build networks; (b) improving social skills; (c) assessing network diagnostics (i.e. using network data or information to inform network strategies); (d) promoting engagement with influential actors; and (e) a process for goal-setting and feedback. The effect of interventions on network structures, or the moderating effect of structure on the intervention effectiveness was rarely assessed. As many natural face-to-face opportunities for social connection are limited due to COVID19, groups already at risk for social isolation and loneliness are disproportionately impacted. Network-building interventions include multiple components that address both the structure of individuals' networks, and their skills and motivation for activating them. These intervention elements could be adapted for delivery via online platforms, and implemented by trained facilitators or novice volunteers, although more rigorous testing is needed.
Background
Disagreements between patients and caregivers about treatment benefits, care decisions, and patients' health are associated with increased patient depression as well as increased caregiver ...anxiety, distress, depression, and burden. Understanding the factors associated with disagreement may inform interventions to improve the aforementioned outcomes.
Methods
For this analysis, baseline data were obtained from a cluster‐randomized geriatric assessment trial that recruited patients aged ≥70 years who had incurable cancer from community oncology practices (University of Rochester Cancer Center 13070; Supriya G. Mohile, principal investigator). Patient and caregiver dyads were asked to estimate the patient's prognosis. Response options were 0 to 6 months, 7 to 12 months, 1 to 2 years, 2 to 5 years, and >5 years. The dependent variable was categorized as exact agreement (reference), patient‐reported longer estimate, or caregiver‐reported longer estimate. The authors used generalized estimating equations with multinomial distribution to examine the factors associated with patient‐caregiver prognostic estimates. Independent variables were selected using the purposeful selection method.
Results
Among 354 dyads (89% of screened patients were enrolled), 26% and 22% of patients and caregivers, respectively, reported a longer estimate. Compared with dyads that were in agreement, patients were more likely to report a longer estimate when they screened positive for polypharmacy (β = 0.81; P = .001), and caregivers reported greater distress (β = 0.12; P = .03). Compared with dyads that were in agreement, caregivers were more likely to report a longer estimate when patients screened positive for polypharmacy (β = 0.82; P = .005) and had lower perceived self‐efficacy in interacting with physicians (β = −0.10; P = .008).
Conclusions
Several patient and caregiver factors were associated with patient‐caregiver disagreement about prognostic estimates. Future studies should examine the effects of prognostic disagreement on patient and caregiver outcomes.
Nearly one‐half (48%) of older patients and caregivers disagree with each other about the patient's prognosis; specifically, 26% of patients and 22% of caregivers report a longer estimate of the patient's prognosis. Caregiver distress, patient communication self‐efficacy, polypharmacy, and oncologist sex are associated with patient‐caregiver agreement about prognostic estimates.
The internet is commonly used by older adults to obtain health information and this trend has markedly increased in the past decade. However, studies illustrate that much of the available online ...health information is not informed by good quality evidence, developed in a transparent way, or easy to use. Furthermore, studies highlight that the general public lacks the skills necessary to distinguish between online products that are credible and trustworthy and those that are not. A number of tools have been developed to assess the evidence, transparency, and usability of online health information; however, many have not been assessed for reliability or ease of use.
The first objective of this study was to determine if a tool assessing the evidence, transparency, and usability of online health information exists that is easy and quick to use and has good reliability. No such tool was identified, so the second objective was to develop such a tool and assess it for reliability when used to assess online health information on topics of relevant to optimal aging.
An electronic database search was conducted between 2002 and 2012 to identify published papers describing tools that assessed the evidence, transparency, and usability of online health information. Papers were retained if the tool described was assessed for reliability, assessed the quality of evidence used to create online health information, and was quick and easy to use. When no one tool met expectations, a new instrument was developed and tested for reliability. Reliability between two raters was assessed using the intraclass correlation coefficient (ICC) for each item at two time points. SPSS Statistics 22 software was used for statistical analyses and a one-way random effects model was used to report the results. The overall ICC was assessed for the instrument as a whole in July 2015. The threshold for retaining items was ICC>0.60 (ie, "good" reliability).
All tools identified that evaluated online health information were either too complex, took a long time to complete, had poor reliability, or had not undergone reliability assessment. A new instrument was developed and assessed for reliability in April 2014. Three items had an ICC<0.60 (ie, "good" reliability). One of these items was removed ("minimal scrolling") and two were retained but reworded for clarity. Four new items were added that assessed the level of research evidence that informed the online health information and the tool was retested in July 2015. The total ICC score showed excellent agreement with both single measures (ICC=0.988; CI 0.982-0.992) and average measures (ICC=0.994; CI 0.991-0.996).
The results of this study suggest that this new tool is reliable for assessing the evidence, transparency, and usability of online health information that is relevant to optimal aging.
Although implementation models broadly recognize the importance of social relationships, our knowledge about applying social network analysis (SNA) to formative, process, and outcome evaluations of ...health system interventions is limited. We explored applications of adopting an SNA lens to inform implementation planning, engagement and execution, and evaluation. We used Health Links, a province-wide program in Canada aiming to improve care coordination among multiple providers of high-needs patients, as an example of a health system intervention. At the planning phase, an SNA can depict the structure, network influencers, and composition of clusters at various levels. It can inform the engagement and execution by identifying potential targets (e.g., opinion leaders) and by revealing structural gaps and clusters. It can also be used to assess the outcomes of the intervention, such as its success in increasing network connectivity; changing the position of certain actors; and bridging across specialties, organizations, and sectors. We provided an overview of how an SNA lens can shed light on the complexity of implementation along the entire implementation pathway, by revealing the relational barriers and facilitators, the application of network-informed and network-altering interventions, and testing hypotheses on network consequences of the implementation.
Workforce development is an important aspect of evidence-informed decision making (EIDM) interventions. The social position of individuals in formal and informal social networks, and the relevance of ...formal roles in relation to EIDM are important factors identifying key EIDM players in public health organizations. We assessed the role of central actors in information sharing networks in promoting the adoption of EIDM by the staff of three public health units in Canada, over a two-year period during which an organization-wide intervention was implemented.
A multi-faceted and tailored intervention to train select staff applying research evidence in practice was implemented in three public health units in Canada from 2011 to 2013. Staff (n = 572) were asked to identify those in the health unit whom they turned to get help using research in practice, whom they considered as experts in EIDM, and friends. We developed multi-level linear regression models to predict the change in EIDM behavior scores predicted by being connected to peers who were central in networks and were engaged in the intervention.
Only the group of highly engaged central actors who were connected to each other, and the staff who were not engaged in the intervention but were connected to highly engaged central actors significantly improved their EIDM behavior scores. Among the latter group, the staff who were also friends with their information sources showed a larger improvement in EIDM behavior.
If engaged, central network actors use their formal and informal connections to promote EIDM. Central actors themselves are more likely to adopt EIDM if they communicate with each other. These social communications should be reinforced and supported through the implementation of training interventions as a means to promoting EIDM.
Health equity research spans various disciplines, crossing formal organizational and departmental barriers and forming invisible communities. This study aimed to map the nomination network of ...scholars at the University of Rochester Medical Center who were active in racial and ethnic health equity research, education, and social/administrative activities, to identify the predictors of peer recognition.
We conducted a snowball survey of faculty members with experience and/or interest in racial and ethnic health equity, nominating peers with relevant expertise.
Data from a total of 121 individuals (64% doing research on extent and outcomes of racial/ethnic disparities and racism, 48% research on interventions, 55% education, and 50% social/administrative activities) were gathered in six rounds of survey. The overlap between expertise categories was small with coincidence observed between education and social/administrative activities (kappa: 0.27;
< 0.001). Respondents were more likely to nominate someone if both were involved in research (OR: 3.1), if both were involved in education (OR: 1.7), and if both were affiliated with the same department (OR: 3.7). Being involved in health equity research significantly predicted the centrality of an individual in the nomination network, and the most central actors were involved in multiple expertise categories.
Compared with equity researchers, those involved in racial equity social/administrative activities were less likely to be recognized by peers as equity experts.
Although dissemination and implementation (D&I) science is a growing field, many health researchers with relevant D&I expertise do not self-identify as D&I researchers. The goal of this work was to ...analyze the distribution, clustering, and recognition of D&I expertise in an academic institution.
A snowball survey was administered to investigators at University of Rochester with experience and/or interest in D&I research. The respondents were asked to identify their level of D&I expertise and to nominate others who were experienced and/or active in D&I research. We used social network analysis to examine nomination networks.
Sixty-eight participants provided information about their D&I expertise. Thirty-eight percent of the survey respondents self-identified as D&I researchers, 24% as conducting D&I under different labels, and 38% were familiar with D&I concepts. D&I researchers were, on average, the most central actors in the network (nominated most by other survey participants) and had the highest within-group density, indicating wide recognition by colleagues and among themselves. Researchers who applied D&I under different labels had the highest within-group reciprocity (25%), and the highest between-group reciprocity (29%) with researchers familiar with D&I. Participants significantly tended to nominate peers within their departments and within their expertise categories.
Identifying and engaging unrecognized clusters of expertise related to D&I research may provide opportunities for mutual learning and dialog and will be critical to bridging across departmental and topic area silos and building capacity for D&I in academic settings.
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