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Structural analysis of health-relevant policy-making information exchange networks in Canada Contandriopoulos, Damien; Benoît, François; Bryant-Lukosius, Denise; Carrier, Annie; Carter, Nancy; Deber, Raisa; Duhoux, Arnaud; Greenhalgh, Trisha; Larouche, Catherine; Leclerc, Bernard-Simon; Levy, Adrian; Martin-Misener, Ruth; Maximova, Katerina; McGrail, Kimberlyn; Nykiforuk, Candace; Roos, Noralou; Schwartz, Robert; Valente, Thomas W; Wong, Sabrina; Lindquist, Evert; Pullen, Carolyn; Lardeux, Anne; Perroux, Melanie Sep 20, 2017

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STUDY PROTOCOL Open AccessStructural analysis of health-relevant policy-making information exchange networks inCanadaDamien Contandriopoulos1, François Benoît2, Denise Bryant-Lukosius3, Annie Carrier4, Nancy Carter3, Raisa Deber5,Arnaud Duhoux1, Trisha Greenhalgh6, Catherine Larouche7, Bernard-Simon Leclerc8, Adrian Levy9,Ruth Martin-Misener10, Katerina Maximova11, Kimberlyn McGrail12, Candace Nykiforuk11, Noralou Roos13,Robert Schwartz14, Thomas W. Valente15, Sabrina Wong12, Evert Lindquist16, Carolyn Pullen17, Anne Lardeux1and Melanie Perroux1*AbstractBackground: Health systems worldwide struggle to identify, adopt, and implement in a timely and system-widemanner the best—evidence-informed—policy-level practices. Yet, there is still only limited evidence about individualand institutional best practices for fostering the use of scientific evidence in policy-making processes The presentproject is the first national-level attempt to (1) map and structurally analyze—quantitatively—health-relevant policy-making networks that connect evidence production, synthesis, interpretation, and use; (2) qualitatively investigate theinteraction patterns of a subsample of actors with high centrality metrics within these networks to develop an in-depthunderstanding of evidence circulation processes; and (3) combine these findings in order to assess a policy network’s“absorptive capacity” regarding scientific evidence and integrate them into a conceptually sound and empiricallygrounded framework.Methods: The project is divided into two research components. The first component is based on quantitative analysisof ties (relationships) that link nodes (participants) in a network. Network data will be collected through a multi-stepsnowball sampling strategy. Data will be analyzed structurally using social network mapping and analysis methods. Thesecond component is based on qualitative interviews with a subsample of the Web survey participants having central,bridging, or atypical positions in the network. Interviews will focus on the process through which evidence circulatesand enters practice. Results from both components will then be integrated through an assessment of the network’sand subnetwork’s effectiveness in identifying, capturing, interpreting, sharing, reframing, and recodifying scientificevidence in policy-making processes.Discussion: Knowledge developed from this project has the potential both to strengthen the scientific understanding ofhow policy-level knowledge transfer and exchange functions and to provide significantly improved advice on how toensure evidence plays a more prominent role in public policies.Keywords: Health-relevant policies, Heath policy, Knowledge exchange, Policy-making, Social network analysis* Correspondence: melanie.perroux@umontreal.ca1Faculté des Sciences Infirmières, Université de Montréal, C.P. 6128 succursaleCentre-ville, Montréal, QC H3C 3J7, CanadaFull 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.Contandriopoulos et al. Implementation Science  (2017) 12:116 DOI 10.1186/s13012-017-0642-4BackgroundNew conceptual and methodological developments inthe broad field of knowledge transfer and exchangesuggest significant improvement in policies and practicescould be achieved by shifting the focus of analysis fromdiscrete interventions to broader information exchangenetworks. This proposal aims to map and analyzehealth-relevant information exchange networks at thenational level in Canada. It will lead to concrete bestpractice recommendations with the potential to improvethe integration of scientific evidence into health-relevantpolicies and practices and ultimately have a positiveimpact on the health of Canadians.Significance and objectives of the researchHealth systems worldwide struggle to identify, adopt,and implement in a timely and system-wide manner thebest—evidence-informed—policies and practices. Thisstruggle, in turn, has significant implications forresources and population health [1–3].A large body of scholarship has focused on developinginterventions to strengthen the influence of scientificevidence on decisions and policies. However, despitesignificant energy and investments, efforts to do so haveproved trickier than initially anticipated [3, 4]. The com-plexity of policy-level1 knowledge transfer and exchange(KTE) interventions has thwarted attempts to producestrong instrumental evidence on the “how-to” [3, 5, 6].Part of the problem is rooted in the fact that much ofthe KTE literature focuses on discrete “interventions.” Inpractice, policy-making processes take place in complexnetworks where actors are interdependent and whereKTE is neither linear nor discrete. Further inquiry intothe composition and functioning of the channelsthrough which information informs practices anddecisions is crucial to identify best practices for fosteringuse of scientific evidence [3, 7–16].This project’s main objective is thus to understand howscientific evidence interconnects with health-relevantpolicy-making processes. Operationally, this will beachieved by focusing on the composition and structure ofcomplex policy networks and then analyzing the processesof information circulation and absorption within thesenetworks. We define health-relevant policies as encom-passing both healthcare policies (i.e., policies about health-care services financing or delivery) and healthy publicpolicies (i.e., intersectoral policies with significant implica-tions for population health and health equity).More specifically, this project adopts a sequentialmixed-methods approach, structured in two componentswith three specific objectives:1. To map and structurallyanalyze—quantitatively—health-relevant policy-making networks that connect evidence production,synthesis, interpretation, and use (component A).2. To select a subsample of actors with high centralitymetrics or interesting structural positions withinthese networks and qualitatively investigate theircommunication and interaction patterns, to developan in-depth understanding of evidence circulationprocesses and related strategies (component B).3. To combine these findings in order to assess a policynetwork’s absorptive capacity regarding scientificevidence and to integrate them into a conceptuallysound and empirically grounded framework(integration of components A and B).Conceptual frameworkConceptually, this project is at the intersection of threefields of research. The first—usually referred to inCanada2 as KTE—is focused on analysis and improve-ment of the bidirectional linkages between scientificevidence production and policy or practice. The secondfield—policy-making analysis—is anchored in politicalscience and public administration and is focused onunderstanding structures and processes that influencepublic policy development, adoption, and implementa-tion, conceptualized as dependent variables. The thirdfield—network analysis—is transdisciplinary, often verymethodologically driven, and focused on networkstructures as independent variables explaining a diverserange of phenomena.Although there is a considerable body of literature ineach of these fields on the influence and use of scientificevidence in policy formulation and making, their inter-section has only been partially explored (e.g., reviewsabout KTE and networks [17]; policy-making andnetworks [18–20]; KTE and policy-making [4, 5]). Few,if any, studies have tapped into cross-learning from allthree. However, developments in those three fieldssupport a redefinition of how policy-related KTEinterventions should be conceptualized. More realisticconceptualizations should take into account that infor-mation exchanges in policy-making processes involveheterogeneous actors (beyond researchers, civil servants,and managers) and are both collective (rather thaninvolving sovereign autonomous decision-makers) andsystemic (rather than step-based, as in linear or circu-lar models).We broadly define policy networks here as the struc-tures and processes of interaction among individuals andorganizations engaged in a policy field [21–23]. Thisdefinition highlights the heterogeneity of policy actorsand arenas. Policy networks are not confined to govern-ment authorities and formal decision-makers but alsoinclude all other actors who work on policies or seek toinfluence, transform, or shape policies, such as non-Contandriopoulos et al. Implementation Science  (2017) 12:116 Page 2 of 11governmental organizations, activists, industries, interestgroups, or the media [24, 25]. In this perspective, net-works do not have formal boundaries; they are informal,self-organizing, and in continual transformation [26–29].By collective, we mean that policy processes occur insystems with a high level of interdependency andinterconnectedness among participants [17, 18, 30].Interdependency here refers to the fact that usually noneof the participants dispose of enough autonomy orpower to translate the information into practices ontheir own [31–39]. In such contexts, individuals are em-bedded within systemic relations, where knowledge usedepends on processes such as sense-making [40–42],coalition-building [8, 43, 44], developing trustworthiness[45, 46], and rhetoric and persuasion [42, 47–49].Such a view calls for a broader conceptualization ofpolicy-making, in both the processes and the actors in-volved. Although the fields of policy analysis and KTEhave been very much influenced by the concepts of“decision” and “decision-making,” operationalizing thoseconcepts in collective systems [37, 50] can be highly prob-lematic [51–53]. In contrast, policy processes are systemic,in that they involve a slowly evolving set of participantsinteracting over long periods [31, 39, 50, 54–60]. Discretedecisions or events are never the end of an identifiableprocess, but rather steps in a broader game [26, 61–64].The sophistication of the policy-making concept sum-marized above highlights the importance of understandinghow the structure formed by policy actors’ interactionswith each other influences the circulation and absorptionof scientific evidence. Converging evidence suggests theconnection between scientific results and policy-makers’practices is strengthened in policy networks/subnetworksin which scientific evidence “sources” or “producers” oc-cupy, on average, a more central position. Based on socialnetwork analysis methods and theories, there is strongconceptual [65–69] and empirical [17, 18, 21, 56, 70–75]evidence to support the hypothesis that actors in bridgingpositions and/or with high centrality wield more influence.Sandström and Carlsson’s work [18, 30, 55], for example,demonstrates that subnetworks with high actor hetero-geneity, high density, and high whole-network centralityare more desirable for effective KTE.Accordingly, our aim in this proposal is to shift thefocus of KTE analysis to (1) the structure of theinterconnections between actors and (2) behaviors andcommunication processes (ties) as core determinants ofthe influence of scientific evidence in policy-makingprocesses.Such a focus prompts a shift in effect attribution. MostKTE literature is based on causal attribution models, inwhich intervention effectiveness is seen as attributableto characteristics of the strategy, users, or producers.However, if the structure of interconnections betweenactors is indeed a core determinant of KTE effectiveness,those attribution models are inappropriate [17, 30, 75].What becomes crucial is understanding the networkstructure and its functioning. As described in moredetail below, this project combines quantitative struc-tural analysis of actors’ positions with qualitative analysisof their behaviors and communication processes from anetwork perspective.MethodsAs highlighted in the previous section, understandingpolicy-related KTE processes implies shifting the focus ofanalysis in two ways: first, by relying on a more realisticconceptualization in which KTE is seen as the product ofcollective and systemic exchange networks of heteroge-neous actors, and second, by combining structuralnetwork analysis with information about actors’ behaviors,resources, and skills in communication processes and ac-tors’ perceptions of their capacity to act upon/influencepolicy-making processes [17]. For this reason, the presentproject will use a mixed-methods approach [76–78] withtwo components: (A) mapping and structurally analyzingCanadian health-relevant policy networks through multi-step snowball sampling and (B) qualitatively analyzing theprocesses through which scientific evidence circulates,based on interviews with a purposeful sample of signifi-cant actors in the network. Results from both componentswill then be integrated into a unified, conceptually sound,and empirically grounded framework (see Fig. 1 for avisual summary of the research design and Fig. 2 for avisual summary of the timeline of research activities).Component A: network mapping and structural analysisThis component is aimed at identifying the actors in-volved in health-relevant policy networks in Canada andanalyzing their structural position within these networks(objective 1). This method is based on quantitativeanalysis of relations (ties) that link nodes (here, individ-ual actors) in a network.Data collection and research participantsNetwork data will be collected through a multi-step snow-ball sampling strategy. The first challenge in implementingsuch an approach is to set conceptually sound andoperationally manageable boundaries for the networkbeing sampled. For this, we will apply two typologies. Thefirst is a typology of actors and spheres of action based onthe policy network literature [45, 79]:– Political sphere: Elected decision-makers at thefederal, provincial, and municipal levels;– Public administration: Civil servants at the federal,provincial, and para-governmental institutionallevels;Contandriopoulos et al. Implementation Science  (2017) 12:116 Page 3 of 11Fig. 1 Example of a sociogramFig. 2 Four-year project timelineContandriopoulos et al. Implementation Science  (2017) 12:116 Page 4 of 11– Academia: Researchers/professors in universities andother, mostly publicly funded, research institutions;– Media: Journalists and other news producers inbroadcast, print, and electronic media;– Civil society: Interest groups, advocacy coalitions,unions, nongovernmental organizations, foundations,transnational agencies, and professional associations;– Private sector: Private corporations and industries.The second typology is focused on the operationaldefinition of what we have described as health-relevantpolicies. Healthcare policies and healthy public policiesinclude a wide range of complex interventions whichoften share few similarities aside from their ultimate goalof positively impacting the health of individuals andpopulations [6]. To structure the delimitation of thefield, we integrated and adapted the OECD typology ofhealth policies with the WHO typology of healthy pol-icies [80–82]. The end result is a heuristic classificationwith no pretension of exhaustively listing all subfields.Its role is to help in the inductive identification of infor-mants in each sphere’s fields and subfields.These two typologies are the starting point of oursnowball strategy. They will be used to build and struc-ture an initial list of actors and organizations consideredto be involved in shaping or trying to influence health-relevant policies in Canada. Sources used to compile aninitial list of names and contact information in each ofthese spheres, per province, will consist of publicly avail-able directories and institutional websites, social mediaplatforms, and reference lists provided by each of theteam’s co-applicants and collaborators based on eachCanadian region. The health-relevant policy fields andsubfields provided in Table 1 will be used as a structureboth to generate keywords for online searches and todefine the boundaries of the data collection effort. Pilottesting of the approach suggests we will be able tocompile initial lists of hundreds of contacts per category.We aim to launch the approach with between 2000 and5000 initial contacts.Potential respondents will be contacted by mail, email,and phone (details below) and invited to complete ashort bilingual online survey structured around fourthemes: (1) provide informed consent, (2) answer a fewdescriptive questions on personal characteristics (specif-ically professional occupation [the sampling category];perceived KTE role(s) [along the producer/broker/userdivision]; institutional affiliation; hierarchical positionheld in the institution; and geographic location), (3)identify the health-relevant policies in which they are in-volved (closed questions built from Table 1 as a startingpoint and finalized after pilot testing; respondents willalso be able to identify other policy themes on whichthey are working by selecting the option “other”), and(4) nominate up to ten people with whom they are incontact regarding their involvement in policy-relevantprocesses. Previous work by the team with this methodsuggests saturation occurs before ten responses [83].Participant eligibility will be based on self-perception,in that any individuals who consider themselves activelyinvolved in health-relevant policy processes at theinstitutional, provincial, or federal levels will be eligible.For every element in the survey, an operational defin-ition will be displayed onscreen using a mouse-overfunction (e.g., for question 4: “Being in contact withis here defined as a regular or irregular form of per-sonal communication, either face-to-face or via email,phone, or social media”). The survey is expected totake around 5 min to complete. We will use the Poli-node platform (www.polinode.com), an online toolspecializing in relationship-based surveys and networkanalysis.Respondents identified through this peer-nominationprocess will, in turn, be invited to fill out the survey andTable 1 Health-relevant policy fields and subfieldsHealthy public policiesPolicies across spheres that explicitly take into account theirimplications for population health and health equityPrevention and healthpromotion- Food and nutrition- Alcohol/tobacco/addiction- Chronic diseases and long-term care- Disease surveillance(communicable andnon-communicable)Social, economic,environmentaldeterminants, and healthequity- Housing- Transport- Education- Income/fiscal policies- Employment- Social assistanceHealthcare policiesPolicies about healthcare services financing or deliveryHealth financing andfunding- Universal health coverage- Payment and insurance systems- Health systems characteristics- Equity and access to health servicesand products- Funding policy- Hospital fundingHealth system servicedelivery- Quality of care- Coordination of care- Primary healthcare- Community care- Home care- Hospital servicesHealth data governanceand infrastructure- Data governance: privacy,monitoring, and research- Strengthening health information- Infrastructure for healthcare qualitygovernance- Data-driven innovation: big data forgrowth and well-beingContandriopoulos et al. Implementation Science  (2017) 12:116 Page 5 of 11identify their own network of contacts. This multi-stepsnowballing process is a common way to identify actorsin unbounded networks, such as policy networks, andhas been used in other studies to identify policy-makersand/or influential actors in policy-making [21, 79]. Thismethod reduces initial sampling bias, since single-stepsampling is generally restricted to actors assumed to bethe most active in formal settings and/or publicly visible.By including other meaningful contacts who are not ne-cessarily the most visible or expected actors, the recur-sive name generation of contacts-of-contacts expandsthe network and captures its heterogeneity.The main challenge of this data collection process is toobtain a satisfactory response rate. Following Dillman’stailored design best practices [84], we will use a sequentialmultiple contact strategy to stimulate participation. Poten-tial participants will receive both personalized email andmail invitations, with a token incentive [85]. Mail andemail reminders will be sent 2 weeks later, followed by aphone call to nonrespondents a week later during whichthey will be given the possibility to respond to the surveyby phone. The project involves partners, co-investigators,and collaborators with extensive contact lists in all prov-inces. These contacts will be used to personalize mail andemail invitations in order to increase the response rate.The snowball data collection period will run for 1 yearof active follow-up, with the objective of obtaining 20 to50% response rates in each policy actor sphere. As thereare no reliable estimates of the number of potentialparticipants in each of these categories, we will usethe Cormack-Jolly-Seber “capture–mark–recapture”model [86], as implemented in the Program MARKsoftware (http://www.phidot.org/software/mark/back-ground/), to calculate the estimated whole populationof actors in each subgroup. Each person identified aspertaining to one of the categories of actors we aimto sample will be considered “captured.” Each personalready on our list whose name emerges through thesnowball name-generator question will be considered“recaptured.” Using this method, total population esti-mates per policy actor sphere will be computed dur-ing data collection. At the end of data collection, themodel will also provide reliable estimates of the pro-portion of the overall network for which we havedata. This is a crucial issue for social network analysis(SNA) ego-based snowball sampling, as the networksobtained are always only a bounded extraction froma, practically, limitless network [21, 87, 88].Data analysis and interpretationThe data obtained through the multi-step snowball sam-pling (names of participants and of the people with whomthey are in contact) will be transposed into a symmetricmatrix where each row/column corresponds to a node(actor) and where values correspond to ties (relationsbetween actors). From such a matrix, it is possible toproduce a network map (sociogram) and to computenetwork-, cluster-, and node-level metrics [68, 87–92]. Todo this, the data will be imported into and analyzed withUCINET 6 software. Sociogram visual optimization will bedone on Cytoscape 3.3.0 software through force-directedalgorithms (see Fig. 1). The data will then be analyzedstructurally using SNA and graph theory [65, 68, 87–92].To identify central actors, actors in bridging positions,and actors with atypical connectivity [65, 68, 91] in thenetworks, node (degree, closeness, betweenness, andeigenvector centralities) and network (density, cluster-ing, and structural holes) structural metrics will becomputed. Actors’ personal characteristics will be plot-ted on the graph to identify shared attribute patterns(homophily) [93]. We will also use multiple regressionmodels (in SPSS 23.0) to test the statistical associationbetween actors’ characteristics and structural nodemetrics. Finally, community detection algorithms willbe used to understand underlying clustering factors.Conceptually coherent clusters (i.e., based on nodehomophily, policy issues, or geographic proximity) willbe identified and treated as policy subnetworks. We willalso measure the interconnectedness of these health-relevant policy subnetworks.Results from this structural analysis will be interpretedat three levels. First, we will assess the whole-networkconnectivity of actors labeled as scientific evidence“sources/producers” in health-relevant policy networksin Canada. Then, we will compare policy subnetworksbased on the assessment criterion that policy subnet-works in which scientific evidence sources are, on aver-age, more central are more desirable. Finally, we willcompare the KTE potential of subnetworks, based onSandström and Carlsson’s work [18, 30, 55] showing thatsubnetworks with high actor heterogeneity, high density,and high whole-network centrality are more desirable.Component B: qualitative analysis of communicationprocesses and perceived influenceAs stated earlier, structural position alone does notexplain how knowledge can be efficiently circulated andtransferred in health policy networks; factors such asconceptual capacity and political clout must also betaken into consideration. For example, an actor may bein a structural position that enhances his/her exposureto relevant information but be ill-equipped, in practicalterms, to make sense of this information [5, 94, 95] or touse it to influence others [5, 14, 47, 96]. Conversely, anactor could have low structural connectivity, and thuslimited exposure to relevant information, but still havesignificant conceptual capacity. We will rely on the con-cept of absorptive capacity to bridge these two notionsContandriopoulos et al. Implementation Science  (2017) 12:116 Page 6 of 11of structural position and conceptual capacity. An actorwith high absorptive capacity [95, 97] has both the op-portunities (high structural exposure to new knowledge[18, 56, 67, 68, 71, 73]) and the means (prior knowledgeand practical capacity [8, 23, 41, 46, 94]) to foster use.To understand actors’ behaviors and informationprocessing strategies through which structural networkconnectivity are operationalized, we will conduct qualita-tive semi-structured interviews with a purposeful sub-sample of the Web survey participants. This will allowus to understand both how actors end up in a particularstructural position and whether actors’ views on theircapacity to access evidence, transfer information, andultimately influence policy-making correspond to thetheoretical advantages that specific network positionsand structures are assumed to provide (e.g., central andbridging positions).Data collectionInformant selection will be based on the actor-level andcluster-level structural metrics obtained from structuralanalysis (component A). For each actor type and subnet-work, we will invite a combination of actors (maximum-variation sampling strategy) with high prestige (degreecentrality and eigenvector centrality), high bridging(betweenness centrality and structural-hole position),and atypical connectivity [65, 68, 91] in the whole net-work and within subgroups/clusters to participate in in-depth semi-structured interviews of approximately60 min. Clusters identified through component A—in-cluding data about each node’s real name/organizationalaffiliation—will be discussed with all co-investigatorsand collaborators to look for ideological or interest-based clustering effect [23, 98, 99]. We plan to conductbetween 40 and 60 such interviews. As informants willbe spread throughout Canada, interviews will be con-ducted either by phone or Skype depending on infor-mants’ preferences. Interviews will be conducted in theinformant’s preferred language and will be recorded(with informed consent), transcribed, proofread, andimported into ATLAS.ti 7 qualitative data analysis soft-ware for coding and analysis.For each participant, the main themes covered will be– Themes/issues/policies in which he/she is involvedin the network.– Role played in the network and modes through whichthis role is enacted (public media appearances,advocacy, participation in public or stakeholderforums, membership in government committees oradvisory groups, provision of direct advice orassistance in policy-making processes, collaborativeresearch, and/or personal communications withofficial policy- and decision-makers).– Networking motives and practices (how did theparticipant come in contact with the people listed inthe name generator survey, how does the participantcreate new contacts, for what reasons, what are theparticipant’s needs/expectations when seeking newcontacts).– Advantages/limitations of network positions(are current network relations useful, which aremost useful and why, level of difficulty inestablishing/finding necessary contacts).– Perceived influence in the network (personalassessment and opinion on which type of actor hasmore influence on policy-making and why, modes ofparticipation in the network that seem more efficientfor using and disseminating scientific evidence, andexternal factors that facilitate or limit individualcapacity to play an effective role in the network,e.g., organizational affiliation, professional occupation,and hierarchical position).Data analysis and interpretationTranscript coding will be based on systematic identifica-tion of recurring themes [100, 101]. Codes will be devel-oped inductively as the analysis unfolds, but the startingpoint will be anchored in the complementary dimen-sions put forward in the works of C Phelps, et al. [17],Sandström and Carlsson [18], and Sabatier and Jenkins-Smith [60]. Discourse analysis approaches [100–102] willbe used. Each coded interview will first be analyzed in-dependently and then transversally, by comparing simi-larities and differences between policy subnetworks andactors’ characteristics. We will use investigator triangula-tion (n = 2) to ensure coding reliability [103, 104]. Code-book definitions and analysis will be scrutinized anddiscussed in group meetings with all co-investigatorsand research assistants. The research team has extensiveexperience successfully using similar qualitative dataanalysis.DiscussionResults integration and impactAs stated in previous sections, the project’s main object-ive is to provide a conceptually sound and empiricallygrounded understanding of the way by which scientificevidence interconnects with decision-making at policylevels in Canada. To achieve this (ambitious) goal, theproject relies on a mixed-method design with two com-ponents. Conceptually, the integration of both compo-nents’ results will involve extending the notion ofabsorptive capacity to the subnetwork level. Absorptivecapacity is conceived here as both a property of subnet-works’ structural properties and the optimization of ac-tors’ communicative strategies within a given structuralarrangement. This extension tallies with existing evidenceContandriopoulos et al. Implementation Science  (2017) 12:116 Page 7 of 11on collective effects in innovation adoption [75, 97] andknowledge use [51, 105]. The results will shed light on therelative structural position of individuals and institutionswithin subnetworks, the communications strategies theyuse, and the factors (interests and ideologies) that explainthem.This project is based on cutting-edge, interdisciplinaryconceptual developments [17, 18, 20, 24, 55, 79] and in-novative large-scale data collection methods. Conceptu-ally, it addresses some of the main challenges that havevexed collective-level knowledge transfer and exchange(KTE) literature. Knowledge developed from this projecthas the potential both to strengthen the scientific under-standing of how collective KTE functions and to generatesignificantly improved practical advice on how tostrengthen the role of evidence in organizational practicesand public policies. Ultimately, this can lead to moreeffective integration of scientific evidence into practicesand to decisions that can have a beneficial impact on thehealth of Canadians.Dissemination of resultsThe KTE plan for this project is threefold. First, thisproject is conceptually innovative and relies on a datacollection approach that has, to the best of our know-ledge, never been used at the national level. We believethe results will lead to high-impact scientific articleswith potential to influence the field.Second, the project involves three key partner organi-zations that will be actively involved in both the use ofthe project’s results (user role) and their disseminationto other potential users (vector role): the National Col-laborating Centre for Healthy Public Policy (NCCHPP),the Canadian Nurses Association (CNA), and the On-tario Tobacco Research Unit (OTRU).Partnerships with our three main partner organiza-tions will be paramount in helping the research teamcontextualize the findings, adapt them to the needs ofusers involved in organizational decision-making andpolicy-making, and disseminate them to relevant stake-holders. Adapted and summarized findings will be for-matted both in a 1:3:25 report and as an interactivewebsite. Beyond the three core partners identified above,we will also mobilize collaborations with the EvidenceNetwork and the six National Collaborating Centres forPublic Health to disseminate results to potential users.In the same way, our team is truly pan-Canadian inscope, and co-investigators’ and collaborators’ connec-tions with other significant actors in Canada will be usedto foster more such collaborations as the projectunfolds.The third element of the plan is that a fundamentalproduct of this project will be the development of anominal map of thousands of actors involved in health-relevant policies across Canada. This map in itself willrepresent a KTE instrument of remarkable possibilities.All individuals identified through the project’s datacollection efforts will receive an email link to the reportand interactive website results. The network map will beuploaded to the website, and participants will be able tolog in and locate their exact position (node), intercon-nections in the network, and personal centrality metrics.This sharing of results with participants also has thepotential to play an important role in disseminatingresults. Active traffic monitoring strategies and socialmedia will also be used to foster access. Team expertiseand resources will also be mobilized for that purpose,especially to attract mass media attention as a part ofthe end-of-grant KTE plan.Expertise, experience, and resourcesThis project is ambitiously integrative at both theconceptual and methodological levels. Essential to itssuccess is a team of researchers with complementaryindividual expertise and, collectively, a truly outstandingtrack record. The pan-Canadian composition of theteam is also a key strength, as team members’ knowledgeof health-relevant policies and policy actors in theirrespective provinces will be essential for designing re-search tools, identifying and reaching out to participants,disseminating findings and results, and adapting them tolocal needs.Endnotes1We define policy-level interventions broadly to includefederal, provincial, and para-governmental institutional-level interventions. A more operational definition is pro-vided in the next pages.2The phenomenon we label knowledge transfer andexchange here is described under a variety of terms de-pending on the country and discipline. Common termsinclude knowledge translation, knowledge exchange, andimplementation research.AbbreviationsCNA: Canadian Nurses Association; KTE: Knowledge transfer and exchange;NCCHPP: National Collaborating Centre for Healthy Public Policy;OECD: Organisation for Economic Co-operation and Development;OTRU: Ontario Tobacco Research Unit; SNA: Social network analysis;WHO: World Health OrganizationAcknowledgementsNot applicableFundingBased on this proposal, the project was awarded a 1-year bridge fundingfrom the Canadian Institutes for Health Research to run a pilot of the datacollection approach (application no. 376601).Availability of data and materialsPlease contact author for data requests.Contandriopoulos et al. Implementation Science  (2017) 12:116 Page 8 of 11Authors’ contributionsDC, MP, AL, and CL have coordinated the writing of the research proposal.All authors have played a key role in conceptualizing research, adaptingdesign for better feasibility, and writing protocol according to their field ofexpertise. All authors read and approved the final manuscript.Ethics approval and consent to participateThe ethics committee of the University of Montreal has approved thisresearch project (17-106-CERES-D).Consent for publicationNot applicableCompeting interestsThe authors declare that they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Faculté des Sciences Infirmières, Université de Montréal, C.P. 6128 succursaleCentre-ville, Montréal, QC H3C 3J7, Canada. 2National Collaborating Centrefor Healthy Public Policy (NCCHPP), 190 Boulevard Crémazie Est, Montréal,QC H2P 1E2, Canada. 3School of Nursing 3H48C, McMaster University, 1280Main Street West, Hamilton, ON L8S 4K1, Canada. 4École de réadaptation,Université Sherbrooke, 3001, 12e Avenue Nord, Sherbrooke, QC J1H 5N4,Canada. 5Institute of Health Policy, Management and Evaluation, University ofToronto, Health Sciences Building, 155 College Street, Suite 425, Toronto, ONM5T 3M6, Canada. 6Nuffield Department of Primary Care Health Sciences,Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK.7Department of Anthropology, Mcgill University, 7th Floor Leacock Building,855 Sherbrooke Street West, Montreal, QC H3A 2T7, Canada. 8InterActions,centre de recherche et de partage des savoirs, 11 822, avenue duBois-de-Boulogne, Montréal, QC H3M 2X7, Canada. 9Department ofCommunity Health and Epidemiology, Centre for Clinical Research, DalhousieUniversity, Room 425, 5790 University Ave, Halifax, NS B3H 1V7, Canada.10School of Nursing, Dalhousie University, Room G26, Forrest Bldg., PO Box15000, 5869 University Ave, Halifax, NS B3H 4R2, Canada. 11School of PublicHealth, 3-300 Edmonton Clinic Health Academy, University of Alberta, 11405– 87 Ave, Edmonton, AB T6G 1C9, Canada. 12UBC Centre for Health Servicesand Policy Research, Vancouver Campus, 201-2206 East Mall, Vancouver, BCV6T 1Z3, Canada. 13Department of Community Health Sciences, Max RadyCollege of Medicine, University of Manitoba, Room S113-50 Bannatyne Ave,Winnipeg, MB R3E 0W3, Canada. 14Dalla Lana School of Public Health,University of Toronto, Health Sciences Building 155 College St., Room 540,Toronto, ON M5T 3M7, Canada. 15Department of Preventive Medicine, Schoolof Medicine, University of Southern California, 2001 N. Soto Ave, Room 302w,Los Angeles, CA 90034, Canada. 16School of Public Administration, Universityof Victoria, PO Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada. 17CanadianNurses Association, 50 Driveway, Ottawa, ON K2P 1E2, Canada.Received: 25 July 2017 Accepted: 31 August 2017References1. Fisher ES, Bynum JP, Skinner JS. Slowing the growth of health carecosts—lessons from regional variation. N Engl J Med. 2009;360(9):849–52.2. Haines A, Kuruvilla S, Borchert M. Bridging the implementation gapbetween knowledge and action for health. Bull World Health Organ. 2004;82(10):724–32.3. Prewitt K, Schwandt TA, Miron L. Straf: using science as evidence in publicpolicy. Washington: Committee on the Use of Social Science, Knowledge inPublic Policy, National Research Council of the National Academies; 2012.4. Mitton C, Adair CE, Mckenzie E, Patten SB, Perry BW. Knowledge transferand exchange: review and synthesis of the literature. Milbank Q.2007;85(4):729–68.5. 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