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Pathways for best practice diffusion: the structure of informal relationships in Canada’s long-term care… Dearing, James W; Beacom, Amanda M; Chamberlain, Stephanie A; Meng, Jingbo; Berta, Whitney B; Keefe, Janice M; Squires, Janet E; Doupe, Malcolm B; Taylor, Deanne; Reid, Robert C; Cook, Heather; Cummings, Greta G; Baumbusch, Jennifer L; Knopp-Sihota, Jennifer; Norton, Peter G; Estabrooks, Carole A Feb 3, 2017

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RESEARCH Open AccessPathways for best practice diffusion: thestructure of informal relationships inCanada’s long-term care sectorJames W. Dearing1*, Amanda M. Beacom2, Stephanie A. Chamberlain2, Jingbo Meng1, Whitney B. Berta3,Janice M. Keefe4, Janet E. Squires5, Malcolm B. Doupe6, Deanne Taylor7, Robert Colin Reid8, Heather Cook9,Greta G. Cummings2, Jennifer L. Baumbusch10, Jennifer Knopp-Sihota11, Peter G. Norton12and Carole A. Estabrooks2AbstractBackground: Initiatives to accelerate the adoption and implementation of evidence-based practices benefit froman association with influential individuals and organizations. When opinion leaders advocate or adopt a bestpractice, others adopt too, resulting in diffusion. We sought to identify existing influence throughout Canada’s long-term care sector and the extent to which informal advice-seeking relationships tie the sector together as a network.Methods: We conducted a sociometric survey of senior leaders in 958 long-term care facilities operating in 11 ofCanada’s 13 provinces and territories. We used an integrated knowledge translation approach to involve knowledgeusers in planning and administering the survey and in analyzing and interpreting the results. Responses from 482senior leaders generated the names of 794 individuals and 587 organizations as sources of advice for improvingresident care in long-term care facilities.Results: A single advice-seeking network appears to span the nation. Proximity exhibits a strong effect on networkstructure, with provincial inter-organizational networks having more connections and thus a denser structure thaninterpersonal networks. We found credible individuals and organizations within groups (opinion leaders and opinion-leading organizations) and individuals and organizations that function as weak ties across groups (boundary spannersand bridges) for all studied provinces and territories. A good deal of influence in the Canadian long-term care sectorrests with professionals such as provincial health administrators not employed in long-term care facilities.Conclusions: The Canadian long-term care sector is tied together through informal advice-seeking relationships thathave given rise to an emergent network structure. Knowledge of this structure and engagement with its opinionleaders and boundary spanners may provide a route for stimulating the adoption and effective implementation of bestpractices, improving resident care and strengthening the long-term care advice network. We conclude that informalrelational pathways hold promise for helping to transform the Canadian long-term care sector.Keywords: Canada, Long-term care sector, Long-term care, Diffusion of innovations, Advice seeking, Social networkanalysis, Opinion leadership, Integrated knowledge translation* Correspondence: dearjim@msu.edu1Department of Communication, Michigan State University, Suite 473, 404Wilson Road, East Lansing, MI 48824-1212, USAFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Dearing et al. Implementation Science  (2017) 12:11 DOI 10.1186/s13012-017-0542-7BackgroundThe diffusion of innovations paradigm [1] suggests thatthe structure of relationships among the members of asocial system, in combination with perceptions of inno-vations and the environmental context in which a socialsystem is embedded, affects the decisions of members toadopt an innovation or not [2–9]. Especially when aninnovation strikes potential adopters as important, theyrely on credible others in their network of relationshipsfor guidance before adoption or rejection [1, 10]. Thisfundamental importance of social influence in decisionprocesses is well-established across academic disciplinesand practical applications [1, 11, 12].Having insight into the informal structure of an advice-seeking network is akin to having a key. Once in hand, thekey can be used by an intervention team to help estimatewhere in a network interventions can best be seeded. Anintervention team can also identify structural weaknesses inthe network—such as a potential relationship that does notyet exist or two groups that might benefit from being tiedtogether—that can be targeted for strengthening in an over-all designing for diffusion framework [13]. The absence of arelationship can represent an opportunity for tying a net-work more closely together. In a complementary fashion,the spread of an innovation once it is introduced into a net-work can be accelerated by well-established relationshipsformed on the basis of co-location, a common background,employment in the same sector, or another basis for peopleto perceive similarity with each other [14]. This is especiallythe case for colleagues who are accessible and whowe perceive to be trustworthy and/or an expert abouta topic—informally influential opinion leaders [15]. In stud-ies of knowledge diffusion, scholars have observed that cen-tral advice sources may act as opinion leaders, driving thespread of knowledge through the network [16, 17].In this study, we sought to identify existing influenceamong directors of care in Canada’s long-term care(LTC; nursing home) sector and the extent to which in-formal advice-seeking relationships among them bindthem as a network that spans the sector. An understand-ing of the extent to which this sector is informally inter-connected could offer a new means for stimulating anational system transformation [18], which is our team’sdistal goal. The sustainability of the LTC sector inCanada depends upon system transformation.The decentralized Canadian contextThe formal structure of the Canadian LTC sector is com-plex and variable across provinces and territories. TheLTC sector in Canada sits outside of the CanadianMedicare System, such that while many components ofthe health care system in Canada are publicly financed,the long-term care sector is financed through a combin-ation of public and private contributions [19]. Provincesare responsible for how long-term care facilities organize,deliver, and monitor care. Their respective approacheshave been shaped by where residential long-term care hasbeen situated in the province’s evolution of health and so-cial policy. Generally, provincial and territorial ministrieshave responsibility for legislation, regulations, standards,and policies. Presently, residential long-term care is situ-ated in the “health” portfolio of provincial governments,with the exception of New Brunswick where it is undersocial development. In a few provinces, ministries ownand operate long-term care facilities (e.g., Prince EdwardIsland) whereas in others, the owner-operator model mayinclude the regional health authority (e.g., Saskatchewan),not-for-profit only (e.g., New Brunswick) or a mix of non-profit and for-profit (e.g., Nova Scotia, Manitoba).From staff and resident perspectives, differences existacross the country as well. Direct care staff vary in howthey are identified, the education requirement for entry topractice, and the extent to which their workplace is union-ized [20]. Entry to LTC is based on provincial/territorial cri-teria, supported by centralized and coordinated entry, andin many jurisdictions, a standardized assessment (theinterRAI-Home Care1) is used as part of the LTC applica-tion process to inform eligibility and priority. All residentspay some portion of accommodation costs, often based ontheir income.Study rationale and purposeIn Canada, as in a number of countries, the proportion ofpeople aged 65 and older is increasing, with projectionssuggesting a very substantial proportion of future popula-tions in this age group. The number of Canadians 65 andolder will more than double to 10.4 million by 2036 [21].Already, Canadian LTC residents are increasingly olderand more frail with multiple chronic conditions and spe-cialized needs, and most have dementia [22].The purpose of our project was to identify existingadvice-seeking networks among LTC facilities that arewithin Canada’s residential LTC sector by using socialnetwork analysis. Our goal was to inform future effortsto disseminate transformative innovations using thisknowledge. Our specific aims were to:1. Identify the structure of existing informal inter-organizational and interpersonal relationships among958 LTC facilities and LTC directors of care inAtlantic, Western, and Northern Canada.2. Identify which LTC facilities and individuals withingroups are most influential and which homes andindividuals within the overall network link differentgroups together.Our team has related aims, not dealt with here, ofexplaining why care improvement advice is sought (throughDearing et al. Implementation Science  (2017) 12:11 Page 2 of 13a qualitative study) and studying the knowledge translationroles of our practitioner colleagues in system transform-ation. Our pan-Canadian team has worked together for sev-eral years within the Translating Research in Elder Care(TREC) program of research that has been explained in thisjournal [23–25] and that relies on a partnership model ofapplied research.Levels of analysis in social networksSocial network metrics allow for the analysis of nodes (sin-gle actors such as a person or an organization), how nodesare tied together, and analysis of networks as a whole. Weconceptualized and wanted to compare an interpersonalnetwork with an inter-organizational network becauseLTC leaders—like actors in other industries—may welllook to both individuals and to organizations when theyare considering the adoption of a care improvementinnovation. We do this while acknowledging that in a LTCleader’s mind, the two are blended; he or she has one ego-centric network in mind consisting of comparative and as-pirational sources [26, 27]. This reference group is likelycomposed of a care director’s set of known colleagues,along with some number of organizations that arewatched and admired. Such organizations may be bothother LTC facilities that are considered progressive orhighly reputable and other types of organizations such asprovincial health departments, quality assurance organiza-tions, and university departments of geriatrics.MethodsStudy populationThe study population consisted of one senior leader(most with the job title of Director of Care or Directorof Nursing) from each of the 958 LTC facilities operatingat the time of data collection in the Canadian provincesof Newfoundland and Labrador, Prince Edward Island,Nova Scotia, and New Brunswick in Atlantic Canada;British Columbia, Alberta, Saskatchewan, and Manitobain Western Canada; and the territories of Yukon,Nunavut, and the Northwest Territories in NorthernCanada. We defined “LTC facility” as a residential long-term care setting for older adults, commonly those aged65 and older, that offers 24-h on-site personal care, nurs-ing care, and housekeeping services. Our facility samplewas a census one. Eligible LTC facilities were first identi-fied using the Guide to Canadian Healthcare Facilities(2012) and then verified via consultation with regionalLTC professionals. We chose the senior leaders becausethey had decision-making responsibility for clinical careand for implementing innovations that influence bestpractice use, evidence-based decision-making, and resi-dent care quality.Data collection and measuresData collection occurred between November 2014 andMay 2015 via distribution of an online survey instrumentand followed the Dillman method of tailored survey de-sign [28]. The survey was available in English and French.To pilot test the survey instrument, we recruited four LTCleaders in Edmonton and six in Atlantic Canada tocomplete the survey and participate in cognitive debrief-ing, with one participant completing the survey in French.Feedback from the pilot testing resulted in refinement ofsurvey format, instructions, and question wording.We designed the survey to take 10 min to complete andincluded questions about advice-seeking behavior, demo-graphics, and current employment and employment his-tory. We assessed advice-seeking behavior at both theinterpersonal and inter-organizational levels. For interper-sonal advice seeking, we asked the participants to list indi-viduals external to their LTC facility whose advice theyseek or behavior they monitor about delivery of qualitycare, care improvement, and innovation. The respondentswere instructed that these interpersonal sources of advicecould include people who work in a LTC facility or thosewho work in another setting such as government, not-for-profit organizations, or industry. The participants couldlist up to three individuals (from most- to second-most-to third-most-valued source of advice) and were asked tospecify the individuals’ job titles and organizational affilia-tions. For inter-organizational advice seeking, we askedthe participants to list LTC facilities whose example orreputation they followed with respect to delivery of qualitycare, care improvement, and innovation. The participantscould list up to three organizations in the order of mostvalued sources.We collected employment data by asking the partici-pants to indicate their primary organizational affiliation, ifthey had responsibility for more than one facility, howlong they had worked in long-term care over their career,and how long they had worked in their position at theircurrent LTC facility. We also asked the participants tospecify whether their primary facility was free-standing orco-located with another health care facility and for the lastthree organizations in which they had worked. We askedfor demographic information on job title, gender, age,highest level of education achieved, and professionalbackground.In addition to the data collected via survey instrument,we collected three variables describing the individuals andLTC facilities in the study population from publicly avail-able records. The first variable was the health authority inwhich each individual worked or in which each LTC facil-ity was located. We used the health authority data toexamine whether patterns in advice-seeking relationshipswere influenced by geographic proximity. The secondvariable was the owner-operator model of each LTCDearing et al. Implementation Science  (2017) 12:11 Page 3 of 13facility in the study. Following the protocol of our parentresearch program, TREC, we classified owner-operatormodels in three categories: public not-for-profit, voluntary(e.g., faith based) not-for-profit, and private for-profit [23].On the advice of regional experts, we used a fourth cat-egory, private not-for-profit, to characterize ownership ofthe majority of LTC facilities in one province, New Bruns-wick. The third variable was the size of each LTC facility,as measured by number of beds in each facility. Again, fol-lowing the TREC protocol, we classified the size in threecategories: small (fewer than 80 beds), medium (80–120beds), and large (more than 120 beds).A final variable for the interpersonal networks, profes-sional role, was created using the job title andorganizational affiliation information collected for eachsurvey participant and each individual nominated as asource of advice.AnalysisWe cleaned the collected survey data to remove duplicateresponses and incomplete responses. Complete responsesprovided the respondent’s name, job title, primary LTC af-filiation, and the nomination of at least one individual ororganization (outside of the respondent’s own focalorganization) as a source of advice. Survey respondentswho reported working at more than one LTC facility wererepresented as one node only in the interpersonalnetwork, but as multiple nodes (one node for each LTCfacility at which the respondent worked) in the inter-organizational network. We then created adjacency matri-ces for the interpersonal and inter-organizationalnetworks in each of the provinces and territories, in which“1” indicated that an advice or social modeling relation-ship existed between two senior leaders or two LTC facil-ities and “0” indicated the absence of a relationship. Thematrices were constructed such that the ego in the dyadwas the advice seeker (the survey respondent or the re-spondent’s primary LTC facility) and the alter was the ad-vice source or model (an individual who the respondentidentified as a valued source of advice or an organizationthat the respondent identified as a model of quality care).We then used the two adjacency matrices as data files inthe network analysis.We performed analyses at two levels: at the level ofeach province and territory and at the pan-Canadian orwhole-network level. The employment and demographicdata were analyzed by calculation of descriptive statis-tics, using SPSS version 23. The network data were ana-lyzed by calculation of network descriptive statistics atthe whole-network, province or territory, and nodal (in-dividual and organizational) levels using SPSS, UCINETversion 6, and Gephi version 0.9. We created network vi-sualizations using Gephi and ArcGIS.At the whole-network level, we measured the numberof types of nodes and ties, density, and in-degreecentralization. Network density is calculated by dividingthe number of observed ties in the network by the numberof possible ties that could exist, if all nodes were con-nected to all other nodes [29]. In many social networks,density is quite low [30] and when considered in isolationthe measure is not particularly meaningful. It does, how-ever, offer a useful measure to compare the relative con-nectedness of a number of different networks, as was ourobjective here with the pan-Canadian analysis.At the nodal level, because of our interest in bestpractice diffusion, we sought to identify individualsand LTC facilities that play key roles in the flow ofadvice through the networks: opinion leaders andboundary spanners. We identified nodes in the inter-personal and inter-organizational networks as opinionleaders on the basis of their in-degree centralityscores. In-degree centrality is a simple count of thenumber of incoming ties, or relationships, a node re-ceives [29]. It is the most commonly used measure ofopinion leadership [31], but little formal consensus existson the appropriate threshold, based on in-degreemeasures, for identifying the number of opinionleaders in a particular network [16, 32]. Accordingly,we tested the appropriateness of several differentthresholds for our data and found that for each pro-vincial and territorial network, an in-degree thresholdof at least two standard deviations above the meanin-degree score of all nodes in the network offeredthe best fit for our data.Boundary spanners are individuals or organizationsthat connect two or more groups in the larger network.Network and diffusion scholars have investigated the as-sociation between diffusion and the presence of bound-ary spanners who “span” structural holes between nodesor groups of nodes in the network [33]. Opinion leaderswho occupy central positions, for example, sometimesact as boundary spanners by virtue of the greater num-bers of others connected to them. They can locate rele-vant and diverse knowledge and then exchange it withothers. Less central actors often also act as boundaryspanners, as in a person peripherally connected to twodifferent groups who acts as a link between the two. Weidentified boundary spanners in the interpersonal andinter-organizational networks using the betweennesscentrality score of each node. Betweenness centrality as-sesses the degree to which a node lies on the shortestpath connecting others in the network. To count thenumber of boundary spanners in each network, we ap-plied the same formula that we used for counting opin-ion leaders: a betweenness centrality threshold of atleast two standard deviations above the mean between-ness centrality score of all nodes in the network.Dearing et al. Implementation Science  (2017) 12:11 Page 4 of 13ResultsSurvey respondentsBecause of response rates of less than 30% in YukonTerritory, Nunavut Territory, and the province ofNewfoundland and Labrador, we excluded data from theseareas from our analysis. From the 926 senior LTC leaderssurveyed in the remaining eight provinces and territories,we collected a total of 482 complete responses for an over-all response rate of 52%. Specific response rates for eachprovince and territory included in the analysis are re-ported in Table 1 and ranged from 41 to 100%.The complete demographic and employment charac-teristics of the survey respondents are also summarizedin Table 1. A majority of the senior LTC leaders whoresponded to the survey were women aged 40 to 59(71%) with a professional background in nursing (79%).Their mean years worked in the long-term care sectorwas about 15 years, and mean years worked in theircurrent position was about 6 years.Network characteristics and measuresResponses from the 482 senior LTC leaders generated thenames of 794 individuals and 587 organizations as sourcesof advice and example for social network analysis. Figure 1presents a visualization of the inter-organizational advicenetwork across Canada, illustrating the geographic scopeof the study and the spatial distribution of advice seeking.Advice relationships extend across provinces and territor-ies to create a single national inter-organizational network,but these inter-provincial relationships are relatively rarecompared with intra-provincial relationships and accountfor only 5% of links in the network. Most of the social in-fluence for care improvement in the long-term care sectorappears to occur intra-provincially and locally. This gen-eral geographic pattern of advice relationships in theinter-organizational network also applies to the interper-sonal network.Tables 2 and 3 present measures describing the struc-tures of the interpersonal and inter-organizational advicenetworks, respectively. The interpersonal advice networkis composed of 1140 individuals, ranging from 19 in theNorthwest Territories to 300 in British Columbia. Thenetwork has 1181 links, with just a small fraction (3%)of these crossing provincial or territorial boundaries; thisreinforces the geographic pattern of advice seeking ob-served in the visualization of the inter-organizationalnetwork displayed in Fig. 1. Interpersonal network dens-ity across all provinces and territories is low, with thehighest densities in the areas with the smaller popula-tions, as is often observed in social networks.We identified 50 opinion leaders in the interpersonaladvice network, with the count in provinces and territoriesranging from 1 to 14. In-degree centrality scores averagedabout 1 for all individuals and about 6 for opinion leaders.Network centralization was highest in the Atlantic prov-inces and in the Northwest Territories and lowest in theWestern provinces. We also identified 51 boundary span-ners in the interpersonal network, with a count across theprovinces and territories ranging from 1 to 13. The aver-age betweenness centrality score was 1 for all individualsand 16 for boundary spanners.Descriptive analysis of the professional role data col-lected for each individual in the interpersonal network isreported in Table 4. As this table indicates, a substantialproportion of individuals in the provinces and territorieshave titles other than LTC senior leader or director ofcare. Many of the individuals nominated as sources ofadvice, in fact, were those working in corporate LTC po-sitions, in regional health authorities and provincial gov-ernments, and in consultant or expert roles in the LTCsector. The British Columbia interpersonal network vi-sualized in the left panel of Fig. 2 illustrates this finding,with nodes in the network color-coded according to pro-fessional role.Table 5 reports on the ownership and size of LTC facil-ities in the inter-organizational advice network. Publicnot-for-profit was the most common owner-operatormodel (43%), then voluntary not-for-profit (24%), and pri-vate for-profit (22%). About half of the LTC facilities inthe network were small, with fewer than 80 beds, and theother half was split equally between medium and large fa-cilities. Inspection of sociograms by province and territory,coded according to ownership and size, suggested no clearinfluence of these variables on network structure.In comparison to the interpersonal advice network,the inter-organizational network had fewer nodes—therespondents named fewer distinct organizations than in-dividuals. Given that the number of possible individualsto name is larger than the number of possible organiza-tions, this is unsurprising. In the interpersonal network,1140 individuals with 1181 links clustered into 87groups; in the inter-organizational network, 792 organi-zations with 1230 links clustered into 19 groups. In eachprovince and territory, network density scores werehigher in the inter-organizational network than in theinterpersonal network. This difference between the twonetworks is illustrated perhaps most dramatically by thedata from British Columbia. Figure 2 depicts a side-by-side comparison of sociograms for the British Columbiainterpersonal and inter-organizational networks, illus-trating that the inter-organizational network appearsmuch more dense and interconnected than the interper-sonal network. Quantitatively, the density score was0.003 for the interpersonal network (Table 2) and 0.020for the inter-organizational network (Table 3).The inter-organizational advice network is similar to theinterpersonal network in highlighting clear opinion-leadingorganizations and boundary spanning organizations. WeDearing et al. Implementation Science  (2017) 12:11 Page 5 of 13identified 39 opinion-leading organizations, with in-degree centrality scores averaging about 2 for all or-ganizations and 6 for opinion-leading organizations.We also identified 50 boundary spanning organiza-tions, with an average betweenness centrality score of13 for all organizations and 100 for boundary span-ning organizations.A second similarity between the interpersonal and inter-organizational networks emerged in analysis of the data onhealth authority geography in each province and territory.At the province and territory level, inspection of socio-grams color-coded by health authority geography suggestedthat opinion-seeking individuals and organizations lookedto others who are geographically proximate to them andTable 1 Response rates and descriptive statistics for survey participants [N (%), except where noted]NS PE NB MB SK AB BC NT M TotalN LTC facilities 88 16 65 128 156 175 290 8 115.75 926Responses 53 (60) 12 (75) 48 (74) 83 (65) 68 (44) 90 (51) 120 (41) 8 (100) 60.25 482 (52)GenderWomen 44 (83) 9 (75) 40 (83) 65 (78) 58 (85) 70 (78) 96 (80) 4 (50) 48.25 386 (80)Men 7 (13) 2 (17) 3 (6) 10 (12) 5 (7) 10 (11) 14 (12) 2 (25) 6.63 53 (11)Missinga 2 (4) 1 (8) 5 (10) 8 (10) 5 (7) 10 (11) 10 (8) 2 (25) 5.38 43 (9)Age20–39 3 (6) 2 (17) 5 (10) 6 (7) 4 (6) 8 (9) 11 (9) 1 (13) 5.00 40 (8)40–59 42 (79) 8 (67) 34 (71) 65 (78) 50 (74) 54 (60) 84 (70) 4 (50) 42.63 341 (71)60+ 7 (13) 1 (8) 7 (15) 8 (10) 10 (15) 20 (22) 17 (14) 1 (13) 8.88 71 (15)Missing 1 (2) 1 (8) 2 (4) 4 (5) 4 (6) 8 (9) 8 (7) 2 (25) 3.75 30 (6)EducationDiploma/certificate 23 (43) 3 (25) 4 (8) 35 (42) 30 (44) 37 (41) 41 (34) 3 (38) 22.00 176 (37)Bachelors 26 (49) 8 (67) 37 (77) 35 (42) 26 (38) 30 (33) 36 (30) 1 (13) 24.88 199 (41)Graduate 3 (6) 0 5 (10) 9 (11) 5 (7) 15 (17) 33 (28) 2 (25) 9.00 72 (15)Missing 1 (2) 1 (8) 2 (4) 4 (5) 7 (10) 8 (9) 10 (8) 2 (25) 4.38 35 (7)Professional backgroundNursing 51 (96) 11 (92) 47 (98) 64 (77) 48 (71) 69 (77) 87 (73) 4 (50) 47.63 381 (79)Business 1 (2) 0 0 9 (11) 10 (15) 5 (6) 12 (10) 0 4.63 37 (8)Other 0 0 0 6 (7) 6 (9) 8 (9) 13 (11) 2 (25) 4.38 35 (7)Missing 1 (2) 1 (8) 1 (2) 4 (5) 4 (6) 8 (9) 8 (7) 2 (25) 3.63 29 (6)Works at >1 facilityNo 50 (94) 9 (75) 47 (98) 67 (81) 57 (84) 79 (88) 102 (85) 7 (88) 52.25 418 (87)Yes 3 (6) 3 (25) 1 (2) 16 (19) 11 (16) 11 (12) 18 (15) 1 (13) 8.00 64 (13)Missing 0 0 0 0 0 0 0 0 0 0Facility managementbStand-alone 45 (85) 9 (75) 45 (94) 61 (73) 50 (74) 65 (72) 89 (74) 3 (38) 45.88 367 (76)Co-located 8 (15) 3 (25) 3 (6) 21 (25) 18 (27) 25 (28) 31 (26) 5 (63) 14.25 114 (24)Missing 0 0 0 1 (1) 0 0 0 0 0.13 1 (0)Years worked [M (SD)]In LTC 14.59 (9.80) 16.23 (10.05) 15.34 (9.24) 16.93 (11.47) 16.52 (9.43) 15.18 (10.63) 14.86 (9.61) 9.33 (11.15) 14.87 −Missing 1 (2) 1 (8) 1 (2) 6 (7) 4 (6) 8 (9) 14 (12) 2 (25) 4.63 37 (8)In current job 6.75 (7.28) 6.32 (5.88) 7.68 (4.95) 4.87 (4.19) 5.58 (6.31) 6.23 (11.77) 4.14 (3.38) 3.30 (3.52) 5.61 −Missing 1 (2) 1 (8) 1 (2) 7 (8) 4 (6) 12 (13) 11 (9) 2 (25) 4.88 39 (8)NS Nova Scotia, PE Prince Edward Island, NB New Brunswick, MB Manitoba, SK Saskatchewan, AB Alberta, BC British Columbia, NT Northwest Territories, LTClong-term careaThe percentage of missing data for each variable was calculated by using as the denominator the total number of responses received in a particular geographicarea. For example, the percentage of missing data for the gender variable in Nova Scotia was 2/53 = 4%bRefers to management model of participant’s primary facility. “Stand-alone” refers to a free-standing facility that has its own management staff, whereas “co-located”refers to a facility that shares management staff and resources with another, typically non-LTC, facilityDearing et al. Implementation Science  (2017) 12:11 Page 6 of 13within their same health authority. In Fig. 2, the sociogramof the British Columbia inter-organizational network in theright panel offers an example of the extent to which seniorleaders look within their own health authority for modelsof care improvement. This result not only offers an import-ant insight for designing best practice dissemination initia-tives in Canadian long-term care but is also not surprising.Geographic proximity often plays an important role in thestructuring of advice and other social networks in numer-ous contexts.LimitationsThese data embed some limitations. Two of Canada’sprovinces (Ontario and Quebec) are not represented, re-sponse rate was partial, and data collection was cross-sectional. While partial response rate to a large voluntarysurvey can always be expected, partial response rates arecause for caution in interpreting social network analyses.Ontario and Quebec, the provinces not yet represented inour data collection, are populous with many LTC facilities,so our structural understanding of advice seeking aboutLTC care improvement currently has this important limi-tation. We hope to address this deficiency in future wavesof data collection. This would also enrich our cross-sectional first take at illuminating the structure of thisrelational network and how it may change as followersidentify new opinion leaders; as individuals retire, relocate,and take on new jobs; and as organizations come and go.As with any data collection procedure, certain aspects ofour data are by-products of instrumentation. For example,the respondents were asked to name three individualswhose advice they most value and three LTC facilitieswhose example they follow, as the basis for social networkanalysis. In consequence, many four-node groups appear,and many nodes appear with three ties to others. Merelychanging the instruction (to two or four) would have al-tered the results but not, we believe, in fundamental ways;“top of the mind” nominations would likely stay the same.Fig. 1 Pan-Canadian inter-organizational network. The black circles represent LTC facilities, and the green and purple lines represent advice relationshipsbetween them. The green lines indicate intra-provincial or territorial relationships, and the purple lines indicate inter-provincial or territorial relationships.Note that Ontario and Quebec were not included in the study sampleDearing et al. Implementation Science  (2017) 12:11 Page 7 of 13Social network data such as we have presented hereshow relationships among people and among organiza-tions as reported by the survey respondents. In socio-grams, the eye is often drawn to those nodes with manyties (high in-degree scores for opinion leaders). What isless obvious are relationships that do not exist in thedata because they were not reported by the respondents.Absence of a tie between two nodes may result fromlack of a relationship or from non-report of an existingrelationship. Because response rate was partial and be-cause respondents could only report up to three individ-uals and three organizations, it is possible that manynodes not linked to each other in our data are in facttied and that groups exhibiting “structural holes” be-tween them actually are tied. While the relational stratawe do see in our data are arguably the most important,because the respondents were instructed to list thoseothers whom they considered most valued, ours is pos-sibly a considerable under-reporting of the actualadvice-seeking network for LTC improvement inCanada. This possibility is particularly so for those ad-vice sources who are not employed in LTC facilities butrather work as provincial administrators, health systemdirectors, and quality assurance experts and in othernon-LTC positions. Because these types of key individ-uals were not in our sampling frame, we have littlesystematic information about them. Anecdotally, how-ever, we and our knowledge translation partners knowor know of these individuals. Through their owninformation-sharing and advice-seeking behaviors, theseauthority figures may function to tie Canada’s LTC sec-tor together more strongly than Fig. 1 shows, at a cross-provincial supra-level that we cannot detect in thepresent data with our sampling frame.DiscussionTransformative system change is necessary in Canada’sLTC sector, given the aging population, health trends ofthose individuals, and the resultant implications for healthcare costs. We believe that if informal opinion leaderswork with formal sector leadership in considering bestpractice adoption and implementation, the care providedin Canadian LTC facilities can be transformed more rap-idly. Accordingly, in this study, we collected sociometric(“who-to-whom”) data from directors of care in CanadianLTC facilities in 11 of Canada’s 13 provinces and territor-ies. Our objective was to describe the extent and structureof advice-seeking networks among these facility directors.Our longer range intent is to combine these data aboutadvice-seeking networks with knowledge translation strat-egies to accelerate the adoption of effective practicesacross Canada’s LTC facilities.Table 2 Measures for interpersonal advice network, by province and territoryNS PE NB MB SK AB BC NT M (SD) TotalNetwork LevelN nodes 135 32 93 181 155 225 300 19 142.50 (95.03) 1140N advice sources 101 25 73 116 103 153 211 12 99.25 (64.91) 794N advice seekers 50 13 47 77 64 88 116 7 57.75 (36.78) 462N ties 134 36 124 195 166 214 296 16 147.63 (92.22) 1181N inter-provincial ties 0 1 2 2 4 11 7 3 3.75 (3.62) 30Density 0.007 0.036 0.014 0.006 0.007 0.004 0.003 0.047 0.016 (0.017)In-degree centralization 0.08 0.13 0.07 0.06 0.05 0.02 0.02 0.19 0.08 (0.06)Nodal levelIn-degree centralityN opinion leadersa 2 1 5 6 12 14 9 1 6.25 (5.01) 50In-degree, all nodes [M (SD)] 0.99 (1.18) 1.13 (1.01) 1.33 (1.42) 1.07 (1.61) 1.07 (1.32) 0.95 (1.01) 0.99 (1.03) 0.84 (0.96) 1.05 (0.14)In-degree, opinion leaders[M (SD)]8.00 (5.66) 5.00 (−) 6.00 (1.23) 8.33 (1.63) 4.75 (1.29) 4.00 (1.04) 5.00 (1.00) 4.00 (−) 5.64 (1.69)Betweenness centralityN boundary spannersb 6 2 6 13 4 9 10 1 6.38 (4.10) 51Betweenness centrality, allnodes [M (SD)]0.50 (1.88) 1.28 (3.00) 7.05 (21.69) 0.24 (0.85) 0.27 (1.35) 0.29 (1.27) 0.42 (1.83) 0.11 (0.46) 1.27 (2.36)Betweenness centrality,boundary spanners [M (SD)]8.50 (2.59) 10.50 (0.71) 84.00 (22.65) 2.96 (1.22) 6.50 (5.74) 5.67 (2.83) 8.95 (4.34) 2.00 (−) 16.14 (27.58)NS Nova Scotia, PE Prince Edward Island, NB New Brunswick, MB Manitoba, SK Saskatchewan, AB Alberta, BC British Columbia, NT Northwest TerritoriesaOpinion leaders were defined as all nodes with in-degree centrality scores of at least two standard deviations above the meanbBoundary spanners were defined as all nodes with betweenness centrality scores of at least two standard deviations above the meanDearing et al. Implementation Science  (2017) 12:11 Page 8 of 13Our results suggest two main themes. First, physicalproximity matters in LTC care improvement advice-seeking. Directors of care seek advice about care im-provement from those who are nearby, both in terms ofbeing employed in the same city and region and in termsof working under the jurisdiction of the same health au-thority. A second, possible proximity effect may manifestin terms of LTC facility ownership, but this is less clearin our data than is grouping by co-location and byhealth authority. Our results suggest that even in the ageof social media and ready online information, care pro-fessionals still look to credible others who tend to bephysically nearby. Of course, even those whose are phys-ically proximate routinely communicate through textmessaging, voice calls, Facebook posts, and email, enjoy-ing an electronic form of proximity in which accessibilityoccurs through social media [34].A second theme in our results is that directors ofcare in LTC facilities learn about ways to improvecare both from conversation with and social modelingby individuals and from monitoring what other orga-nizations are doing and advocating. With this blendedindividual and organizational reference group, direc-tors of care can float the idea of adopting a new prac-tice in their LTC facilities, comparing how care ispursued in their facilities with the aspirational stand-ard of reference group facilities and recommendations.They do this through talk and messaging and observa-tion, as well as through looking to see how the newpractice is being received by other organizations. Arethey trying it? Do they think it is a good idea? Theuse of reference groups by individuals for help in de-ciding whether to try a new practice is a reason whywe asked the respondents for several interpersonal ad-vice sources and several organizational advice sources.Taken together, any one director of care’s answersgives us a glimpse of their reference group for issuesof care improvement.Table 3 Measures for inter-organizational advice network, by province and territoryNS PE NB MB SK AB BC NT M (SD) TotalNetwork LevelN nodes 80 20 59 133 119 151 217 13 99.00(69.47)792N advice sources 66 16 52 99 85 103 158 8 73.38(49.15)587N advice seekers 48 14 49 90 69 78 119 8 59.38(37.55)475N ties 129 36 139 240 187 181 303 15 153.75(96.74)1230N inter-provincial ties 3 4 2 8 11 11 15 7 7.63(4.53)61Density 0.020 0.095 0.041 0.014 0.013 0.008 0.006 0.096 0.037(0.038)In-degree centralization 0.07 0.18 0.13 0.10 0.11 0.03 0.03 0.26 0.11(0.08)Nodal levelIn-degree centralityN opinion leadersa 4 1 4 6 3 11 9 1 4.88(3.60)39In-degree, all nodes [M (SD)] 1.61(1.44)1.80(1.47)2.36(2.20)1.81(1.99)1.60(1.92)1.20(1.25)1.40(1.37)1.15(1.28)1.62(0.39)In-degree, opinion leaders [M (SD)] 5.75(0.96)5.00(−)8.75(0.96)8.17(3.06)10.33(4.04)4.36(0.50)5.56(0.53)4.00(−)6.49(2.30)Betweenness centralityN boundary spannersb 7 2 3 9 5 6 16 2 6.25(4.65)50Betweenness centrality, all nodes[M (SD)]27.09(57.38)7.90(11.88)37.53(67.65)12.38(26.45)4.99(16.50)7.62(22.76)4.54(10.40)0.85(2.08)12.86(12.75)Betweenness centrality, boundaryspanners [M (SD)]185.38(32.01)36.25(3.18)274.44(61.88)94.94(25.01)67.00(43.27)101.25(43.06)35.53(10.61)5.50(0.71)100.04(89.43)NS Nova Scotia, PE Prince Edward Island, NB New Brunswick, MB Manitoba, SK Saskatchewan, AB Alberta, BC British Columbia, NT Northwest TerritoriesaOpinion leaders were defined as all nodes with in-degree centrality scores of at least two standard deviations above the meanbBoundary spanners were defined as all nodes with betweenness centrality scores of at least two standard deviations above the meanDearing et al. Implementation Science  (2017) 12:11 Page 9 of 13Table 4 Professional roles for individuals in interpersonal advice network [N (%), except where noted]NS PE NB MB SK AB BC NT M TotalN individuals in network 135 32 93 181 155 225 300 19 142.50 1140Senior leadership position in an LTC facility (e.g., directorof care)a81 (60) 18 (56) 58 (62) 113 (62) 90 (58) 125 (56) 160 (53) 7 (37) 81.50 652 (57)Position in corporate level of an organization providing LTC 10 (7) 2 (6) 3 (3) 9 (5) 1 (1) 29 (13) 19 (6) 0 9.13 73 (6)Chief executive officer/president/vice president 2 2 2 1 0 8 3 0 2.25 18Quality improvement/clinical services 3 0 1 2 0 9 3 0 2.25 18General director/regional leader 5 0 0 6 1 12 13 0 4.63 37Position in regional health authority or government 12 (9) 7 (22) 5 (5) 34 (19) 43 (28) 43 (19) 54 (18) 5 (26) 25.38 203 (18)Director, seniors health/continuing care 1 1 0 10 13 31 19 2 9.63 77Director, education/best practice/ quality improvement 0 2 0 6 3 12 5 1 3.63 29Manager, case coordination/care coordination/access 5 2 5 2 2 0 10 0 3.25 26Licensing and review 1 0 0 3 0 0 9 0 1.63 13Other 5 2 0 13 25 0 11 2 7.25 58Other position/affiliation 32 (24) 5 (16) 27 (29) 25 (14) 21 (14) 28 (12) 67 (22) 7 (37) 26.50 212 (19)Therapist, physical/occupational/ recreational 0 0 3 1 0 3 3 1 1.38 11Mental health clinician, therapist/behavioral 6 0 2 1 0 3 6 2 2.50 20Educator, best practice/clinical practice 3 0 1 4 4 2 9 0 2.88 23Specialist, wound care/infection control 1 0 1 3 2 3 10 0 2.50 20Clinician, physician/pharmacist/nurse 15 2 8 5 4 5 13 2 6.75 54Other 7 3 12 11 11 12 26 2 10.50 84NS Nova Scotia, PE Prince Edward Island, NB New Brunswick, MB Manitoba, SK Saskatchewan, AB Alberta, BC British Columbia, NT Northwest Territories, LTClong-term careaPercentages are provided for the four main categories of professional roles only, and not for the specific job titlesFig. 2 Interpersonal and inter-organizational networks in British Columbia. The interpersonal network (left) is color-coded by an individual’sorganizational affiliation, and the inter-organizational network (right) is color-coded by LTC facility geographic location. Nodes are sized accordingto in-degree centrality score, such that larger nodes have higher in-degree scores and the largest nodes represent opinion leaders. LTC long-termcare, HA health authority, BC British ColumbiaDearing et al. Implementation Science  (2017) 12:11 Page 10 of 13Our separation of interpersonal sources of advice frominter-organizational sources of advice highlights structuraldifferences between them and variance across provinces. Ingeneral, we see higher degrees of integration (density) inour inter-organizational sociograms than we do with ourinterpersonal sociograms. Figure 2 gives an example of thisgeneral pattern that we see in our data for other provinces,too. At provincial levels, inter-organizational ties appearstronger than interpersonal ties, with fewer small groups oftwo, three, or four nodes that appear unconnected to largeradvice-seeking structures. This makes sense; even if a dir-ector of care does not know a particular person at anotherLTC facility, she can still look to that facility as a source ofideas. The converse is very unlikely because directors ofcare will almost always know the organization to which anindividual belongs. We believe that this is a novel operatio-nalization of social influence and one that in this case pro-duces findings that have high utility for decision-makers inthe health system. Knowing which individuals and whichorganizations collectively are best positioned to help in adissemination and change effort is quite advantageous.What we do not know from the present analysis is thestrength of belief or credibility that advice seekers vest inindividuals versus organization. This is an aim of our tan-dem qualitative study. Knowing a friend and colleaguemight be expected to outweigh the influence of knowingwhat an organization is doing because social exchangescarry unspecified obligations to one another [35] that canaccumulate into strong trusting relationships [36].We are working with our knowledge translation col-leagues in Canada’s provinces and territories to further in-terpret these results with the benefit of professionalinsight. We are also discussing ways in which LTC leadersmay find unique value in data such as these for their ownpurposes in training, continuing education, and strategicdecision-making.ConclusionsOur results suggest that a single advice-seeking network onthe topic of improving resident care in long-term care(nursing home) facilities spans the nation of Canada.Advice-seeking relationships are relatively strong withinprovince and weaker between province, with identifiableopinion leaders and boundary spanners. Proximity exhibitsa strong effect on network structure, with provincial inter-organizational networks having more connections and thusa denser structure than interpersonal networks. We foundcredible individuals and organizations within groups (opin-ion leaders and opinion-leading organizations) and individ-uals and organizations that function as weak ties acrossgroups (boundary spanners and bridges) for all studiedprovinces and territories. Considerable influence in theCanadian long-term care sector rests with professionalssuch as provincial health administrators not employed inlong-term care facilities. Taken together, these are nontrivialand actionable results for our goal of working collabora-tively with Canadian long-term care leaders to improve thestate of practice in this critical sector.Endnotes1http://www.interrai.org/home-care.htmlAbbreviationsLTC: Long-term care; RAI: Resident Assessment Instrument; TREC: TranslatingResearch in Elder CareTable 5 Owner-operator model and number of beds for LTC facilities in inter-organizational advice network [N (%)]NS PE NB MB SK AB BC NT M TotalN LTC facilities in network 80 20 59 133 119 151 217 13 99.00 792Owner-operatorPublic not-for-profit 11 (14) 8 (40) 1 (2) 73 (55) 87 (73) 69 (46) 82 (38) 10 (77) 42.63 341 (43)Private for-profit 38 (48) 8 (40) 0 17 (13) 4 (3) 46 (30) 60 (28) 0 21.63 173 (22)Voluntary not-for-profit 30 (38) 2 (10) 1 (2) 40 (30) 24 (20) 32 (21) 61 (28) 0 23.75 190 (24)Private not-for-profita NA NA 55 (93) NA NA NA NA NA 6.88 55 (7)Missingb 1 (1) 2 (10) 2 (3) 3 (2) 4 (3) 4 (3) 14 (6) 3 (23) 4.13 33 (4)No. Beds0–79 44 (55) 9 (45) 41 (69) 74 (56) 87 (73) 73 (48) 76 (35) 7 (54) 51.38 411 (52)80–120 15 (19) 1 (5) 5 (8) 24 (18) 13 (11) 21 (14) 60 (28) 0 17.38 139 (18)>120 14 (18) 0 11 (19) 25 (19) 10 (8) 35 (23) 47 (22) 0 17.75 142 (18)Missing 7 (9) 10 (50) 2 (3) 10 (8) 9 (8) 22 (15) 34 (16) 6 (46) 12.50 100 (13)NS Nova Scotia, PE Prince Edward Island, NB New Brunswick, MB Manitoba, SK Saskatchewan, AB Alberta, BC British Columbia, NT Northwest Territories, LTClong-term careaApplicable to New Brunswick onlybThe percentage of missing data for each variable was calculated by using as the denominator the total number of LTC facilities in a particular geographic area(i.e., the first number in the column). For example, the percentage of missing data for the owner-operator variable in Nova Scotia was 1/80 = 1%Dearing et al. Implementation Science  (2017) 12:11 Page 11 of 13AcknowledgementsWe thank Charlotte Berendonk of the University of Alberta and SachaNadeau of Mount Saint Vincent University for assisting with data collection,Joe Welsh of Michigan State University for preparing Fig. 1, Pamela Fanceyof the Nova Scotia Centre on Aging for providing information on Canadianlong-term care, and Cathy McPhalen of thINK Editing Inc. for editing the finalversion of the manuscript. We are indebted to our practitioner partners andcollaborators across Canada’s LTC sector who advised us regarding surveydevelopment and administration and in the interpretation of results.FundingThis study was funded by the Canadian Institutes of Health Research,Partnerships for Health System Improvement (MOP #318861). Additionalpartner funding was provided by the Nova Scotia Health ResearchFoundation, Alberta Innovates Health Solutions, the Michael SmithFoundation for Health Research, and Research Manitoba.Availability of data and materialsThe data supporting the conclusions of this article are housed in the secureand confidential Health Research Data Repository (HRDR) in the Faculty ofNursing at the University of Alberta (https://uofa.ualberta.ca/nursing/research/research-supports-and-services/hrdr), in accordance with the healthprivacy legislation of participating TREC jurisdictions. Data specific to thismanuscript can be requested through the TREC Data ManagementCommittee (joseph.akinlawon@ualberta.ca) on the condition that researchersmeet and comply with the TREC, HRDR, and provincial data confidentialitypolicies.Authors’ contributionsJWD, WBB, JMK, JES, HC, JLB, JKS, PGN, and CAE contributed to theconception and design of the study. CAE and JMK secured the CIHR funding,and with assistance of key team members in the provinces, secured thepartner funds. JWD, SAC, WBB, JMK, JES, JLB, PGN, and CAE collaborated onthe data acquisition, and SAC oversaw the data cleaning. AMB conductedthe data analysis in consultation with JWD, SAC, and JM. All authorscontributed to the conceptual discussions and interpretation of the analyticresults, and JWD and AMB wrote the manuscript based on these discussions.WBB provided substantive edits, and JM, JMK, MBD, JES, DT, RCR, GGC, andJKS also provided detailed editorial revisions. All authors read and approvedthe final manuscript.Competing interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approvalWe obtained ethics approval for the study from all participating Canadianuniversities (University of Alberta, Pro00050958; University of British Columbia,H14-02543; University of Saskatchewan, 14-370; University of Manitoba,H2014:370; Mount Saint Vincent University, 2014:043). We also received ethicsand operational approvals and research licenses from all participating provincialand territorial health authorities.Author details1Department of Communication, Michigan State University, Suite 473, 404Wilson Road, East Lansing, MI 48824-1212, USA. 2Faculty of Nursing,University of Alberta, Edmonton, Alberta, Canada. 3Dalla Lana School ofPublic Health, University of Toronto, Toronto, Ontario, Canada. 4Nova ScotiaCentre on Aging, Mount Saint Vincent University, Halifax, Nova Scotia,Canada. 5School of Nursing, University of Ottawa, Ottawa, Ontario, Canada.6Manitoba Center for Health Policy, University of Manitoba, Winnipeg,Canada. 7Research Department, Interior Health Authority, Kelowna, BritishColumbia, Canada. 8School of Health and Exercise Sciences, University ofBritish Columbia Okanagan, Kelowna, British Columbia, Canada. 9Hospitalsand Communities Integrated Services, Interior Health Authority, Kelowna,British Columbia, Canada. 10School of Nursing, University of British Columbia,Vancouver, British Columbia, Canada. 11Faculty of Health Disciplines,Athabasca University, Athabasca, Alberta, Canada. 12Department of FamilyMedicine, University of Calgary, Calgary, Alberta, Canada.Received: 29 October 2016 Accepted: 25 January 2017References1. Rogers EM. Diffusion of innovations. 5th ed. New York: Free Press; 2003.2. Wejnert B. Integrating models of diffusion of innovations: a conceptualframework. Annu Rev Sociol. 2002;28:297–326.3. Greenhalgh T, Robert G, Bate P, Macfarlane F, Kyriakidou O. Diffusion ofinnovations in health service organisations: a systematic literature review.Malden: Blackwell; 2005.4. Coleman JS, Katz E, Menzel H. The diffusion of an innovation amongphysicians. Sociometry. 1957;20:253–70.5. Palinkas L, Holloway I, Rice E, Fuentes D, Wu Q, Chamberlain P. Socialnetworks and implementation of evidence-based practices in public youth-serving systems: a mixed-methods study. Implement Sci. 2011;6:113.6. Cho Y, Hwang J, Lee D. Identification of effective opinion leaders in thediffusion of technological innovation: a social network approach. TechnolForecast Soc Chang. 2012;79:97–106.7. Sibthorpe B, Glasgow N, Wells R. Emergent themes in the sustainability ofprimary health care innovation. Med J Aust. 2005;183:S77.8. Jippes E, Achterkamp M, Brand P, Kiewiet D, Pols J, vanEngelen J.Disseminating educational innovations in health care practice: trainingversus social networks. Soc Sci Med. 2010;70:1509–17.9. West E, Barron D, Dowsett J, Newton J. Hierarchies and cliques in the socialnetworks of health care professionals: implications for the design ofdissemination strategies. Soc Sci Med. 1999;48:633–46.10. Dearing J. Applying diffusion of innovation theory to interventiondevelopment. Res Soc Work Pract. 2009;19:503–18.11. Katz E, Lazarsfeld PF. Personal influence: the part played by people in theflow of mass communication. Glencoe: The Free Press; 1955.12. Rogers E, Cartano D. Methods of measuring opinion leadership. Public OpinQ. 1962;26:435-41.13. Dearing J, Smith D, Larson R, Estabrooks C. Designing for diffusion of abiomedical intervention. Am J Prev Med. 2013;44:S70–6.14. Rogers E, Bhowmik D. Homophily-heterophily: relational concepts forcommunication research. Public Opin Q. 1970;34:523–38.15. Weimann G. The influentials: people who influence people. Albany: SUNYPress; 1994.16. Flodgren G, Parmelli E, Doumit G, Gattellari M, O’Brien MA, Grimshaw J,Eccles MP. Local opinion leaders: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2011;8.17. Valente TW. Network interventions. Science. 2012;337:49–53.18. Berta W, Virani T, Bajnok I, Edwards N, Rowan M. Understanding wholesystems change in health care: Insights into system level diffusion fromnursing service delivery innovations—a multiple case study. Evid Policy.2014;10:313–36.19. MacDonald M. Regulating individual charges for long-term residential carein Canada. Stud Polit Econ. 2015;95:83–114.20. Faust K. Animal social networks. In: Scott J, Carrington PJ, editors. The SAGEhandbook of social network analysis. London: SAGE Publications; 2011. p. 148–66.21. Canada S. Population projections for Canada, provinces and territories 2009to 2036. Ottawa: Minister of Industry; 2010.22. Hirdes J, Mitchell L, Maxwell C, White N. Beyond the ‘iron lungs ofgerontology’: using evidence to shape the future of nursing homes inCanada. Can J Aging. 2011;30:371–90.23. Estabrooks C, Squires J, Cummings G, Teare G, Norton P. Study protocol forthe Translating Research in Elder Care (TREC): building context—anorganizational monitoring program in long-term care project (project one).Implement Sci. 2009;4:52.24. Estabrooks C, Hutchinson A, Squires J, Birdsell J, Cummings G, Degner L,Morgan D, Norton P. Translating research in elder care: an introduction to astudy protocol series. Implement Sci. 2009;4:51.25. Rycroft-Malone J, Dopson S, Degner L, Hutchinson A, Morgan D, Stewart N,Estabrooks C. Study protocol for the Translating Research in Elder Care(TREC): building context through case studies in long-term care project(project two). Implement Sci. 2009;4:53.26. Hyman H, Singer E. Readings in reference group theory and research. NewYork: Free Press; 1968.27. Merton R. Reference groups, invisible colleges, and deviant behavior inscience. In: O’Gorman H, editor. Surveying social life: papers in honor ofHerbert H. Hyman. Middletown: Wesleyan University Press; 1988. p. 174–89.Dearing et al. Implementation Science  (2017) 12:11 Page 12 of 1328. Dillman DA, Smyth JD, Christian LM. Internet, mail, and mixed-mode surveys:the tailored design method. 3rd ed. Hoboken: John Wiley & Sons; 2009.29. Wasserman S, Faust K. Social network analysis: methods and applications.Cambridge: Cambridge University Press; 1994.30. Newman MEJ. Networks: an introduction. Oxford: Oxford University Press; 2010.31. Valente TW. Social networks and health: models, methods, and applications.New York: Oxford University Press; 2010.32. Valente T, Pumpuang P. Identifying opinion leaders to promote behaviorchange. Health Educ Behav. 2007;34:881–96.33. Burt RS. Structural holes. Cambridge: Harvard University Press; 1992.34. Monge PR, Contractor NS. Theories of communication networks. Oxford:Oxford University Press; 2003.35. Blau P. Exchange and power in social life. New York: Wiley; 1964.36. Ring P. Processes facilitating reliance on trust in inter-organizationalnetworks. In: Ebers M, editor. The formation of inter-organizational networks.Oxford: Oxford University Press; 2002. p. 113–45.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Dearing et al. Implementation Science  (2017) 12:11 Page 13 of 13


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