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Facilitated interprofessional implementation of a physical rehabilitation guideline for stroke in inpatient… Salbach, Nancy M; Wood-Dauphinee, Sharon; Desrosiers, Johanne; Eng, Janice J; Graham, Ian D; Jaglal, Susan B; Korner-Bitensky, Nicol; MacKay-Lyons, Marilyn; Mayo, Nancy E; Richards, Carol L; Teasell, Robert W; Zwarenstein, Merrick; Bayley, Mark T Aug 1, 2017

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RESEARCH Open AccessFacilitated interprofessionalimplementation of a physical rehabilitationguideline for stroke in inpatient settings:process evaluation of a cluster randomizedtrialNancy M. Salbach1,11*, Sharon Wood-Dauphinee2, Johanne Desrosiers3, Janice J. Eng4, Ian D. Graham5,Susan B. Jaglal1, Nicol Korner-Bitensky2, Marilyn MacKay-Lyons6, Nancy E. Mayo7, Carol L. Richards8,Robert W. Teasell9, Merrick Zwarenstein10, Mark T. Bayley11 and on behalf of the Stroke Canada Optimization ofRehabilitation By Evidence – Implementation Trial (SCORE-IT) TeamAbstractBackground: The Stroke Canada Optimization of Rehabilitation by Evidence-Implementation Trial (SCORE-IT)showed that a facilitated knowledge translation (KT) approach to implementing a stroke rehabilitation guidelinewas more likely than passive strategies to improve functional walking capacity, but not gross manual dexterity,among patients in rehabilitation hospitals. This paper presents the results of a planned process evaluation designedto assess whether the type and number of recommended treatments implemented by stroke teams in each groupwould help to explain the results related to patient outcomes.Methods: As part of a cluster randomized trial, 20 rehabilitation units were stratified by language and allocatedto a facilitated or passive KT intervention group. Sites in the facilitated group received the guideline with treatmentprotocols and funding for a part-time nurse and therapist facilitator who attended a 2-day training workshop andpromoted guideline implementation for 16 months. Sites in the passive group received the guideline excludingtreatment protocols. As part of a process evaluation, nurses, and occupational and physical therapists, blinded to studyhypotheses, were asked to record their implementation of 18 recommended treatments targeting motor function,postural control and mobility using individualized patient checklists after treatment sessions for 2 weeks pre- and post-intervention. The percentage of patients receiving each treatment pre- and post-intervention and between groups wascompared after adjusting for clustering and covariates in a random-effects logistic regression analysis.(Continued on next page)* Correspondence: nancy.salbach@utoronto.ca1Department of Physical Therapy, University of Toronto, 160-500 UniversityAve, Toronto, ON M5G 1V7, Canada11Toronto Rehabilitation Institute-University Health Network, 550 UniversityAvenue, room 3-131 (3-East) 3rd Floor University Wing, Toronto, ON M5G2A2, 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.Salbach et al. Implementation Science  (2017) 12:100 DOI 10.1186/s13012-017-0631-7(Continued from previous page)Results: Data on treatment implementation from nine and eight sites in the facilitated and passive KT group,respectively, were available for analysis. The facilitated KT intervention was associated with improved implementationof sit-to-stand (p = 0.028) and walking (p = 0.043) training while the passive KT intervention was associated withimproved implementation of standing balance training (p = 0.037), after adjusting for clustering at patient and providerlevels and covariates.Conclusions: Despite multiple strategies and resources, the facilitated KT intervention was unsuccessful in improvingintegration of 18 treatments concurrently. The facilitated approach may not have adequately addressed barriers tointegrating numerous treatments simultaneously and complex treatments that were unfamiliar to providers.Trial registration: Unique identifier-NCT00359593Keywords: Implementation, Facilitation, Interprofessional, Stroke, Rehabilitation, Guideline, Randomized controlled trial,Cluster randomization, Knowledge translation, Process evaluationBackgroundStroke is a chronic disabling condition [1] that isexpected to affect an increasing number of individualsdue to population growth and aging [2]. Clinical practiceguidelines for inpatient stroke rehabilitation settingsprovide clear treatment recommendations aimed atimproving motor function, postural control, and mobil-ity [3–5]. Despite evidence that guideline adherence isassociated with functional recovery [6] and patient satis-faction [7], studies conducted in Canada and in the UKreveal that guideline application is inconsistent [8–11].To narrow these knowledge to practice gaps, the Know-ledge to Action (KTA) Process [12] recommends an evalu-ation of barriers to knowledge use and tailoring ofknowledge translation (KT) strategies to address thosebarriers. Research conducted by our team and others hasshown that implementation of a stroke rehabilitationguideline in the inpatient hospital setting presents uniquechallenges [13, 14]. Owing to a broad research base [15,16], stroke rehabilitation guidelines consist of an extensivenumber of treatment recommendations [3]. Althoughmultiple efficacious treatments may be appropriate for apatient, there may be insufficient time to apply all of themthus requiring individual health professionals to prioritizeand select [13]. This process is complicated by the recom-mendation to deliver stroke rehabilitation by a core inter-disciplinary team consisting of physical and occupationaltherapists, nurses, and speech-language pathologists,physiatrists/physicians, social workers, and dietitians [3].Team functioning and communication may affect howwell members prioritize and coordinate implementationof treatment [13]. The complexity of efficacious treat-ments in terms of the number of steps and technical skillrequired also varies widely which has implications for edu-cation and training [13]. Finally, stroke can lead to hetero-geneous profiles of sensorimotor, communication, andcognitive impairment that cause variable levels of disabil-ity. The type and magnitude of stroke-related deficits, theincidence of shoulder pain which occurs in almost 30% ofpatients [17], and patient preferences can further influencehealth providers’ decisions to apply a recommended treat-ment [13, 18].A multi-component intervention is required to over-come the challenges to integrating stroke rehabilitationguidelines targeting improvement in motor function,postural control, and mobility. Facilitation, defined as“the process of enabling (making easier) the implemen-tation of evidence into practice” (p. 579) [19, 20] is arecognized strategy and core component of the PARiHS(Promoting Action on Research Implementation inHealth Services) framework [20–22] that could poten-tially enable collaboration within stroke teams to imple-ment a comprehensive stroke rehabilitation guideline.Reviews of facilitation [19, 23] have characterized it asboth an individual role incorporating project manage-ment and leadership and a process involving individualsand teams. Facilitators may use a range of strategies toassist individuals and teams to apply evidence in practiceas facilitation should be tailored to the needs of the localcontext [19, 22, 23]. For example, a local facilitator mayorganize training sessions to address a need to buildclinicians’ capacity to implement a specific treatmentand check-in at regular intervals to help maintain motiv-ation levels. Facilitators may also approach managers toenable the purchase of equipment or the re-organizationof therapy space if these are the issues hindering practicechange. In the context of inpatient stroke rehabilitation,employing multiple facilitators recruited from core healthprofessional groups involved in interdisciplinary stroke re-habilitation teams was considered a novel and potentiallyeffective strategy for enabling guideline implementation.In rehabilitation research, guideline provision combinedwith interactive educational sessions to review best prac-tices has been associated with improved performance ofrecommended practices compared to mailing of theguideline among rehabilitation providers [24, 25]. PreviousSalbach et al. Implementation Science  (2017) 12:100 Page 2 of 11trials [24–26], however, have targeted adherence to ≤10recommendations in one professional group and providedlimited description of clustering effects and adjustmentfor covariates. No studies examined use of local facilitatorsfrom different professional groups to promote guidelineimplementation. The Stroke Canada Optimization ofRehabilitation by Evidence Implementation Trial (SCORE-IT) was a cluster randomized trial designed to evaluate theextent to which a multi-modal, facilitated KT approach toimplementing a stroke rehabilitation guideline was morelikely than passive strategies, such as mailing the guidelineand educational materials, to improve patient function inthe inpatient rehabilitation hospital setting (Bayley MT,Wood-Dauphinee S, Richards CL, Salbach NM, DesrosiersJ, Eng JJ, et al.: Facilitated knowledge translation improvesstroke rehabilitation outcomes: The SCORE-IT clusterrandomized controlled trial, under review) [27]. Amongpatients with stroke treated at facilitated KT sites(n = 410), the odds of demonstrating a high level of func-tional walking capacity, measured using the 6-min walktest, were 1.63 times (95% CI: 1.23–2.17) the oddsobserved among patients at passive KTsites (n = 367) (Bay-ley MT, Wood-Dauphinee S, Richards CL, Salbach NM,Desrosiers J, Eng JJ, et al.: Facilitated knowledge translationimproves stroke rehabilitation outcomes: The SCORE-ITcluster randomized controlled trial, under review). The fa-cilitated KT intervention was not associated with grossmanual dexterity (measured using the Box and Block Test)among patients with stroke (OR: 1.69, 95% CI: 0.72–4.01).A mixed methods process evaluation was completed tohelp explain the results related to patient outcomes. In thequalitative process evaluation [27], focus groups were con-ducted with 33 nurses, therapists, and managers from 11of the 20 study sites in the facilitated and passive KTgroups to explore their experiences with SCORE-IT. Thequalitative analysis yielded four themes describing factorsthat facilitated or hindered implementation of the KT in-terventions and clinical integration of the recommendedtreatments [27]. Themes included: presence/absence of fa-cilitation, agreement that the intervention was practical,familiarity with the recommended treatments, and envir-onmental factors (e.g., staff turnover, lack of space orequipment) [27]. Facilitating factors, such as the presenceof an individual who provided stroke teams with supportand motivation throughout the trial, and experience withusing some of the treatment interventions, were describedby participants in both study groups [27]. A fifth theme,namely improved team communication and interdisciplin-ary collaboration, was identified as an unexpected positivetrial outcome that served to facilitate the clinical applica-tion of treatment interventions in both study groups [27].While results from the qualitative process evaluation haveincreased our understanding of site, provider and treat-ment characteristics that may have influencedimplementation of the study interventions and recom-mended treatments, the extent to which eachrecommended treatment was implemented has not beenreported and may help to explain why the facilitated KTintervention was associated with improved functionalwalking capacity but not gross manual dexterity amongpatients. Thus, this paper presents the results of a quanti-tative process evaluation of SCORE-IT designed to evalu-ate the extent to which stroke teams implemented therecommended treatments targeting upper extremity (UE)and lower extremity (LE) motor function, postural control,and mobility in each intervention group.MethodsA national, 2-parallel group cluster-randomized, pragmatictrial was conducted from 2007 to 2009. The effectivenessof a facilitated and passive KT intervention for implement-ing a stroke rehabilitation guideline was evaluated by com-paring patient outcomes related to walking capacity andmanual dexterity post-intervention. To understand howimplementation of guideline recommendations may haveinfluenced study outcomes related to patient function,stroke teams in each intervention group were asked tocomplete self-report checklists to record their implemen-tation of 18 recommended treatments with each patientseen over a 2-week period pre- and post-intervention. Theethics board at each site and affiliated university approvedthe study protocol.Eligibility and recruitmentSites with designated rehabilitation beds, an interdiscip-linary stroke team with at least one nurse, one physicaltherapist (PT) and one occupational therapist (OT), andregular inpatients post-stroke (i.e., ≥1 person post-strokeon the unit daily), were considered eligible. Sites withthese characteristics were targeted as the treatment recom-mendations were developed for implementation primarilyby nurses, PTs, and OTs in an inpatient rehabilitationsetting [13]. Recruitment involved study leads sendingletters of invitation to site managers/physiatrists and inter-viewing to screen for eligibility and obtain consent. Alltherapists and nurses working on the stroke rehabilitationunit were eligible to participate. A research assistant (RA)hired for each site obtained informed consent fromrehabilitation providers. Details of patient eligibility and re-cruitment are described elsewhere (Bayley MT, Wood-Dauphinee S, Richards CL, Salbach NM, Desrosiers J, EngJJ, et al.: Facilitated knowledge translation improves strokerehabilitation outcomes: The SCORE-IT cluster random-ized controlled trial, under review).Data collectionFollowing recruitment, site representatives were asked tocomplete a site readiness checklist that required them toSalbach et al. Implementation Science  (2017) 12:100 Page 3 of 11provide information on the language of documentation(English/French), university affiliation (full/partial or none),rehabilitation unit location (freestanding/integrated with ageneral hospital), and stroke patient volume (expectednumber of stroke patients/year). Site RAs abstracted patientsociodemographic and clinical data from health records.The outcome was change in the percentage of patientsfor which inpatient stroke teams implemented eachrecommended treatment pre- to post-intervention. Allinpatients with stroke were expected to need the majorityof treatments. For select treatments that are applied onlywhen indicated (e.g., to reduce hand edema/shoulderpain), a similar proportion in each group was expected torequire each treatment owing to randomization. A self-report checklist was created for therapists to documentname, profession, and which of the 18 recommendedtreatments was implemented for each patient. To mitigatesocial desirability bias [28], a section was added to thechecklist where therapists could indicate that a treatmentwould have been implemented if time had permitted. Asimilar checklist was created for nurses to report onimplementation of 7 treatments (sit-to-stand training, useof LE external support, walking practice, UE range of mo-tion and/or stretching, interventions to prevent shoulderpain, task-specific training of the UE, and education of pa-tients/caregivers on how to handle the affected UE). SiteRAs oriented therapists and nurses, who were blinded tostudy hypotheses, to the checklists and asked them tocomplete a checklist at the end of every treatment sessionwith patients post-stroke during a two-week period pre-and post-intervention.RandomizationA biostatistician, not involved in study recruitment ordata collection, used R™ statistical software to stratifyhospitals by language of documentation (English/French)and randomly assign them to either the facilitated orpassive KT group using a 1:1 allocation ratio. Site staffwere informed of their group assignment following com-pletion of pre-intervention data collection on treatmentimplementation.InterventionsThe SCORE-IT interventions are described in detailelsewhere (Bayley MT, Wood-Dauphinee S, RichardsCL, Salbach NM, Desrosiers J, Eng JJ, et al.: Facilitatedknowledge translation improves stroke rehabilitationoutcomes: The SCORE-IT cluster randomized controlledtrial, under review) [27]. Intervention development wasguided by the KTA process and by the results of a quali-tative study in which implementation of the stroke re-habilitation guideline was piloted at five inpatientrehabilitation hospitals in Canada [13]. Analysis of tran-scripts from focus groups involving 79 rehabilitationprofessionals (physical and occupational therapists,nurses, and directors/managers) identified lack of time,staffing issues, training/education, therapy selection andprioritization, equipment availability, and team function-ing/communication as key barriers to guideline imple-mentation. In alignment with the KTA process, thefacilitated KT intervention was designed to address thesebarriers. The facilitated KT intervention included fund-ing for two local facilitators, one nurse and one therap-ist, to provide 4 h per week of protected time to supportguideline implementation over a 16-month period. Hav-ing a facilitator from both nursing and allied health wasexpected to facilitate interdisciplinary collaboration andaddress barriers related to team functioning and com-munication [13]. Facilitators attended a two-day work-shop where they received media releases for promotingthe guideline among clinicians, slide presentations of thetreatment protocols, and training in how to apply treat-ments and run small group education/training sessions.This was designed to prepare facilitators to run localsmall group education/training sessions to address bar-riers related to inadequate education/training in how toapply the treatments in clinical practice for existing andnew staff. Facilitators were also provided with an outlineof strategies used to foster guideline implementation in thepilot study [13], a practice-change toolkit [29], and educa-tion in change management. They completed activities tocompare current with recommended practice, identify bar-riers to practice change, and develop a plan that incorpo-rated behavior change strategies to address localchallenges to guideline implementation. This was expectedto prepare facilitators to address other site-specific barriersrelated to, for example, insufficient equipment and motiv-ation to change practice [13]. Stroke teams were providedwith SCORE guideline booklets that included treatmentrecommendations and evidence-based treatment protocolsand pocket reminder cards designed for therapists ornurses to enable quick and easy access to descriptions ofprotocols. These resources were expected to address bar-riers related to inadequate knowledge of and time to readrecommendations [13]. Teleconferences and a web-basedplatform were provided for facilitators to communicateand share successful strategies.Sites in the passive group received SCORE guidelinebooklets that did not include treatment protocols, and ahandbook [30] and educational video on the use of stan-dardized assessment tools post-stroke. Clinicians wereinvited to join a list serve to ask questions and sharetheir experiences with the trial.Sample sizePost hoc power calculations were performed. Given 1381observations available to analyze treatment implementa-tion by nurses and therapists, accounting for clustering ofSalbach et al. Implementation Science  (2017) 12:100 Page 4 of 11observations within patients (mean patient-level intraclus-ter correlation coefficient (ICC) across treatments of 0.12;mean cluster size of 8 observations per patient) yielded aneffective sample size of 751 independent observations(375 per group) [31]. With 375 observations per group(2-sided alpha = 0.05) and a baseline implementation rateof 30%, there was 80% power to detect a between-groupdifference of 10% in the rate of treatment implementation.Given 547 observations available to analyze treatmentimplementation by therapists alone, accounting for clus-tering of observations within patients (mean patient-level ICC across treatments of 0.09; mean cluster size offour observations per patient) yielded an effective samplesize of 431 independent observations (215 per group).With 215 observations per group (2-sided alpha = 0.05)and a baseline implementation rate of 10%, there was80% power to detect a between-group difference of 10%in the rate of treatment implementation.AnalysisThe unit of analysis was a binary variable that repre-sented whether a patient received a recommended treat-ment or not during a treatment session. To account forpotential clustering effects at the level of the hospital,provider, and patient, a random-effects logistic regres-sion analysis was carried out in SAS v9.3. The analysisincluded the following steps. First, estimates of theunadjusted rate of treatment implementation withineach group pre- and post-intervention, change pre- topost-intervention, and between-group comparison ofchange were obtained using proc. nlmixed. Next, testsfor random variation at the site, provider and patientlevels were performed. A significant test result (α = 0.05)in more than 33% of models was the criterion for includinga clustering variable in all final models.A final model was constructed for each treatment withintervention group, evaluation time (pre or post), an inter-action term of group by time, clustering variables andcovariates entered as independent variables using proc.glimmix. Covariates included site location (freestanding/integrated with a general hospital), and size of stroke ser-vice (expected #stroke patients/year), and patient motorfunction (Functional Independence Measure [32] (FIM)motor subscore) and comorbidity (Charlson score [33])on admission. A significant interaction term (α = 0.05)was used to indicate whether change in the extent towhich patients received a treatment was greater followingthe facilitated than following the passive KT interventionafter adjusting for clustering and covariates.ResultsFigure 1 presents the CONSORT diagram describing theresults of recruitment, randomization, and data collec-tion. Of the 20 participating sites, 10 were randomizedto the facilitated KT intervention and 10 were random-ized to the passive KT intervention. Facilitators from allsites in the facilitated group attended the training work-shop (Bayley MT, Wood-Dauphinee S, Richards CL, Sal-bach NM, Desrosiers J, Eng JJ, et al.: Facilitatedknowledge translation improves stroke rehabilitationoutcomes: The SCORE-IT cluster randomized controlledtrial, under review). Three sites were removed from theFig. 1 CONSORT flowchartSalbach et al. Implementation Science  (2017) 12:100 Page 5 of 11analysis because they had no data (n = 2) or only pre-intervention data (n = 1) due to technical issues with thedatabase. Of the three sites removed, two were fromthe passive group and were non-academic, and locatedin a general hospital with 86 and 35 expected patientswith stroke/year. The third site removed was from thefacilitated group; it was a freestanding site, partially-affiliated with a university, with an expected volume of90 patients with stroke/year. Thus, data from nine andeight sites in the facilitated and passive group, respect-ively, were analyzed. The CONSORT diagram indicatesthe number of unique providers and patients involvedin this process evaluation, and the number of checklistssubmitted by providers pre- and post-intervention bystudy group.Table 1 describes characteristics of sites that providedprocess data. Just over half of the sites in the facilitatedand passive groups had no academic affiliation and werefreestanding rehabilitation hospitals. The expected num-ber of patients with stroke admitted per year for sites inthe facilitated and passive groups was, on average, 95and 105, respectively. Table 2 describes characteristics ofthe patients for whom treatment implementation check-lists were completed by intervention group and samplingtime point. The median age of patients ranged from 62to 73 years (depending on group, timepoint, andreceived treatments), with the majority being men (52–69%), ischemic stroke (64–76%), with some arm(CMSA median 2–3) and leg impairment (CMSA me-dian 3). Charlson comorbidity score and the proportionof patients with ischemic stroke were significantlyhigher in the passive group pre-intervention and post-intervention, respectively.Additional file 1: Table S1 describes checklist comple-tion by provider group (see Additional file 1). Nurses con-tributed the greatest percentage of checklists in theTable 1 Site characteristicsCharacteristic Intervention groupFacilitated (n = 9) Passive (n = 8)English-language, n (%) 7 (78) 7 (88)Academic affiliation, n (%)None 5 (56) 4 (50)Partial 1 (11) 1 (13)Full 3 (33) 3 (38)Freestanding, n (%) 5 (56) 4 (50)Expected number of strokepatients/year, mean ± SD (Range)95 ± 49(22–160)105 ± 72(30–210)Table 2 Patient characteristics on site admission by intervention group and sampling time pointCharacteristic 7 Treatments Implemented by RNs, OTs, and PTs 11 Treatments implemented by OTs, and PTs(score range/units) Facilitated group Passive group Facilitated group Passive groupPre* Post* Pre* Post* Pre* Post* Pre* Post*Patients, n 49 40 31 44 40 40 28 40Age in years 62 (57–77) 68 (60–78) 71 (62–79) 72 (65–79) 64 (57–77) 68 (60–78) 73 (62–79) 72 (64–79)Men, n (%) 34 (69) 26 (65) 16 (52) 25 (57) 27 (68) 26 (65) 15 (54) 22 (55)Type of stroke,† n (%)Ischemic 37 (76) 25 (64)‡ 20 (67) 30 (68)‡ 30 (75) 25 (64)‡ 19 (70) 26 (65)‡Hemorrhagic 8 (16) 12 (31) 6 (20) 5 (11) 6 (15) 12 (31) 6 (22) 5 (13)Unspecified 4 (8) 2 (5) 4 (13) 9 (20) 4 (10) 2 (5) 2 (7) 9 (23)Side of stroke, n (%)Right 25 (51) 25 (63) 14 (45) 20 (45) 20 (50) 25 (63) 13 (46) 18 (45)Left 22 (45) 14 (35) 15 (48) 23 (52) 19 (48) 14 (35) 13 (46) 21 (53)Brainstem 2 (4) 1 (3) 2 (6) 1 (2) 1 (3) 1 (3) 2 (7) 1 (3)Days post-stroke on admission 20 (13–28) 15 (9–26) 23 (11–41) 16 (10–34) 20 (13–28) 15 (9–26) 24 (11–44) 15 (10–29)Charlson Index (0–33) 2 (1–3)§ 2 (1–3) 3 (2–4)§ 3 (2–4) 2 (1–3)‡ 2 (1–3) 3 (2–4)‡ 3 (1–4)CMSA Arm|| (1–7) 2 (1–3) 3 (2–5) 2 (2–4) 2 (2–5) 2 (1–4) 3 (2–5) 2 (2–4) 2 (2–5)CMSA Leg|| (1–7) 3 (3–4) 3 (3–5) 3 (2–4) 3 (3–4) 3 (3–5) 3 (3–5) 3 (3–4) 3 (3–4)FIM motor (1–91) 33 (24–54) 46 (36–59) 35 (24–57) 48 (35–59) 32 (22–56) 46 (36–59) 41 (25–58) 49 (35–61)Abbreviations: CMSA Chedoke McMaster Stroke Assessment [30], FIM functional independence measure [32]*Values are median (P25-P75) unless otherwise specified†Data from 1 to 2 patients/analysis missing‡Between-group difference, p < 0.050§Between-group difference, p < 0.010||Data from 13 to 17 patients/analysis missingSalbach et al. Implementation Science  (2017) 12:100 Page 6 of 11Table 3 Unadjusted intervention effect on change in implementation of 18 treatmentsTreatment Time Estimated % of times implemented (95% CI) Effectn Facilitated (F) group n Passive (P) group (ChangeF-ChangeP)% (95% CI)1. Sit-to-stand*†‡§ Pre 647 20.4 (17.3, 23.5) 193 36.3 (29.5, 43.1)Post 276 39.1 (33.4, 44.9) 265 33.6 (27.9, 39.3)Change 18.7 (12.2, 25.3) −2.7 (−11.5, 6.2) 21.4 (10.4, 32.4)2. LE ROM and/or stretching (i.e. toprevent spasticity and contractures)‡Pre 151 15.9 (10.1, 21.7) 118 8.5 (3.4, 13.5)Post 143 10.5 (5.5, 15.5) 135 17.8 (11.3, 24.2)Change −5.4 (−13.1, 2.3) 9.3 (1.1, 17.5) −14.7 (−26.0, −3.5)||3. Use of LE external support (i.e. brace)‡§ Pre 647 7.3 (5.3, 9.3) 193 15.0 (10.0, 20.1)Post 276 8.7 (5.4, 12.0) 265 17.4 (12.8, 21.9)Change 1.4 (−2.5, 5.3) 2.3 (−4.5, 9.1) −0.9 (−8.7, 6.9)4. Task-specific training (i.e. stairs)*†‡ Pre 151 31.8 (24.3, 39.2) 118 26.3 (18.3, 34.2)Post 143 38.5 (30.5, 46.5) 135 37.8 (29.6, 46.0)Change 6.7 (−4.3, 17.6) 11.5 (0.1, 22.9) −4.8 (−20.6, 11.0)5. Training for sitting balance*† Pre 151 23.8 (17.0, 30.7) 118 17.0 (10.2, 23.7)Post 143 17.5 (11.2, 23.7) 135 25.2 (17.9, 32.5)Change −6.4 (−15.6, 2.9) 8.2 (−1.8, 18.2) −14.6 (−28.2, −1.0)||6. Training for standing balance†‡ Pre 151 51.7 (43.7, 59.6) 118 36.4 (27.7, 45.1)Post 143 52.5 (44.2, 60.7) 135 60.0 (51.7, 68.3)Change 0.8 (−10.7, 12.2) 23.6 (11.6, 35.6) −22.8 (−39.4, −6.2)7. FES for the LE† Pre 151 0.7 (−0.6, 2.0) 118 0 (0, 0)Post 143 0.7 (−0.7, 2.1) 135 0.7 (−0.7, 2.2)Change 0 (−1.9, 1.9) 0.7 (−0.7, 2.2) −0.7 (−3.1, 1.7)8. Walking practice†‡§ Pre 647 15.9 (13.1, 18.7) 193 31.6 (25.0, 38.2)Post 276 39.1 (33.4, 44.9) 265 32.8 (27.2, 38.5)Change 23.2 (16.8, 29.6) 1.2 (−7.4, 9.9) 22.0 (11.2, 32.8)9. Treadmill walking practice† Pre 151 2.7 (0.1, 5.2) 118 6.8 (2.2, 11.3)Post 143 1.4 (−0.5, 3.3) 135 5.2 (1.4, 8.9)Change −1.3 (−4.5, 2.0) −1.6 (−7.5, 4.3) 0.3 (−6.4 7.1)10. UE ROM and/or stretching (i.e. toprevent spasticity and contractures)*†‡§Pre 647 12.7 (10.1, 15.2) 193 21.8 (15.9, 27.6)Post 276 21.4 (16.5, 26.2) 265 25.3 (20.1, 30.5)Change 8.7 (3.2, 14.2) 3.5 (−4.3, 11.4) 5.2 (−4.4, 14.7)11. Interventions to prevent shoulderpain (i.e. sling)*‡§Pre 647 25.0 (21.7, 28.4) 193 25.4 (19.2, 31.5)Post 276 25.7 (20.6, 30.9) 265 21.1 (16.2, 26.1)Change 0.7 (−5.5, 6.8) −4.3 (−12.1, 3.6) 4.9 (−5.1, 14.9)12. Task-specific training (i.e. self-caretasks)*†‡§Pre 647 28.9 (25.4, 32.4) 193 37.3 (30.5, 44.1)Post 276 40.9 (35.1, 46.8) 265 43.4 (37.4, 49.4)Change 12.0 (5.3, 18.8) 6.1 (−3.0, 15.2) 6.0 (−5.4, 17.3)13. Techniques to reduce hand edema*†§ Pre 151 7.3 (3.1, 11.4) 118 10.2 (4.7, 15.6)Post 143 5.6 (1.8, 9.4) 135 8.9 (4.1, 13.7)Change −1.7 (−7.3, 3.9) −1.3 (−8.6, 6.0) 0 (−9.6, 8.8)14. Ice/heat or soft tissue massagefor shoulderPre 151 1.3 (−0.5, 3.2) 118 8.5 (3.4, 13.5)Post 143 2.8 (0.1, 5.5) 135 5.2 (1.4, 8.9)Change 1.5 (−1.8, 4.7) −3.3 (−9.6, 3.0) 4.8 (−2.3, 11.8)Salbach et al. Implementation Science  (2017) 12:100 Page 7 of 11facilitated KT group (50 and 42%, pre- and post-interven-tion, respectively) and in the passive KT group (39 and49%, pre- and post-intervention, respectively) for treat-ments that RNs, OTs, and PTs were asked to apply.Additional file 1: Table S2 provides the ICC value forthe effect of clustering on treatment implementation atthe site, provider and patient level for each of the 18treatments. A significant effect of clustering on treat-ment implementation was observed at the site, providerand patient level in 0, 67, and 39% of models, respectively.The median ICC across treatments for sites, providers andpatients was 0.06, 0.21, and 0.08, respectively.Additional file 1: Table S3 presents mean cluster sizesin terms of the number of providers per site, number ofpatients per provider, and number of checklist forms perpatient pre- and post-intervention by study group (seeAdditional file 1). Cluster sizes at the site level indicatedthat the average number of providers contributing data tothe analysis across groups and timepoints ranged from 10to 15 for 7 treatments implemented by RNs, OTs, and PTsand from 5 to 7 for 11 treatments implemented by OTsand PTs. The average number of patients per providercontributing data to the analysis across groups, time-points, and treatments ranged from 2 to 3. The averagenumber of checklists completed per patient in the analysisacross groups and timepoints ranged from 6 to 13 for 7treatments implemented by RNs, OTs, and PTs and from3 to 4 for 11 treatments implemented by OTs and PTs.OutcomesTable 3 presents unadjusted estimates of the percentageof patients receiving each treatment pre- and post-intervention, the change in the percentage, and thebetween-group comparison. Seven of the 18 treatments,including training of sit-to-stand, sitting balance, andstanding balance, task-specific training (i.e., stairs),walking practice, interventions to prevent shoulderpain, and task-specific training (i.e., self-care tasks),were being implemented at least 15% of the time inboth groups at baseline.After adjusting for clustering at patient and providerlevels and covariates, the facilitated KT intervention wasassociated with a significant improvement in the imple-mentation of sit-to-stand training (p = 0.028) and walkingpractice (p = 0.043), and the passive KT intervention wasassociated with improved implementation of standingbalance training (p = 0.037). Adjustment for the stratifica-tion variable did not change the interpretation of the re-sults. Further analysis of which provider groups changedtheir practice (see Additional file 1: Table S4) revealed thatthe unadjusted percentage of patients receiving sit-to-stand training was higher post- compared to pre-intervention for nurses (30 vs 10%), and PTs (67 vs 49%)in the facilitated group, and for PTs (58 vs 39%) in thepassive group. The unadjusted percentage of patientsreceiving walking practice was higher post- compared topre-intervention for nurses (14 vs 6%), OTs (37 vs 21%)and PTs (80 vs 70%) in the facilitated group, and fornurses (14 vs 11%), OTs (25 vs 14%) and PTs (76 vs 68%)in the passive group. The unadjusted percentage ofpatients receiving standing balance training was higherpost- compared to pre-intervention for OTs (36 vs 34%)in the facilitated group, and OTs (38 vs 27%), andPTs (82 vs 45%) in the passive group.Table 3 Unadjusted intervention effect on change in implementation of 18 treatments (Continued)15. FES for wrist/arm/shoulder*† Pre 151 2.0 (−0.2, 4.2) 118 2.5 (−0.3, 5.4)Post 143 1.4 (−0.5, 3.3) 135 1.5 (−0.6, 3.5)Change −0.6 (−3.5, 2.4) −1.1 (−4.6, 2.4) 0.5 (−4.1, 5.1)16. Educate patient or caregiver onhow to handle arm or shoulder*†‡Pre 647 8.8 (6.6, 11.0) 193 13.0 (8.2, 17.7)Post 276 9.4 (6.0, 12.9) 265 10.2 (6.5, 13.8)Change 0.6 (−3.5, 4.7) −2.8 (−8.8, 3.2) 3.4 (−3.9, 10.6)17. UE constraint-induced therapy Pre 151 4.6 (1.3, 8.0) 118 10.2 (4.7, 15.6)Post 143 0.7 (−0.7, 2.1) 135 4.4 (1.0, 7.9)Change −3.9 (−7.6, −0.3) −5.7 (−12.2, 0.8) 1.8 (−5.6, 9.2)18. Visual imagery to enhancearm recovery‡Pre 151 2.7 (0.1, 5.2) 118 5.1 (1.1, 9.1)Post 143 6.3 (2.3, 10.3) 135 5.2 (1.4, 8.9)Change 3.6 (−1.1, 8.4) 0.1 (−5.4, 5.6) 3.5 (−3.7, 10.8)Abbreviations: n number of observations, CI confidence interval, LE lower extremity, ROM range of motion, FES functional electrical stimulation, UE upper extremityItalic text indicates statistically significant results*Demonstration and opportunity to practice during change management workshop†Clinical protocol provided in SCORE guideline‡Clustering effect at provider level§Clustering effect at patient level||No longer significant after adjusting for clustering at the provider and patient levelSalbach et al. Implementation Science  (2017) 12:100 Page 8 of 11DiscussionThis is among the first process evaluations of a guidelineimplementation trial involving the use of interprofes-sional local facilitators in rehabilitation. Findings indi-cate that a facilitated KT intervention, with local nurseand therapist facilitators, tailoring of strategies toaddress local barriers, and a guideline with treatmentprotocols, was of limited effectiveness compared to passiveguideline dissemination in improving short-term uptake ofa comprehensive guideline by inpatient stroke rehabilita-tion teams. The process evaluation revealed that the facili-tated KT intervention was associated with improvedapplication of only two treatments (sit-to-stand training,walking practice), whereas the passive KT interventionwas associated with improved application of one treatment(standing balance training). Results from this processevaluation suggest that superior functional walkingcapacity observed among patients post-stroke following thefacilitated compared to the passive KT intervention (BayleyMT, Wood-Dauphinee S, Richards CL, Salbach NM, Des-rosiers J, Eng JJ, et al.: Facilitated knowledge translation im-proves stroke rehabilitation outcomes: The SCORE-ITcluster randomized controlled trial, under review) resultedfrom an improved application of sit-to-stand and walkingtraining by stroke teams. Process evaluation findings alsoindicate that the facilitated KT intervention was not associ-ated with improved gross manual dexterity among patientsbecause this intervention was not associated with improveduptake of treatments targeting UE function.Results from the current evaluation combined with find-ings from the qualitative process evaluation of SCORE-IT[27] may help to explain why sit-to-stand and walkingpractice were more likely than other treatments to beadopted in the facilitated KT group. Facilitation, specific-ally support and motivation that individuals provided tostaff at sites in each group, was perceived to promote theimplementation of the recommended treatments [27]. It ispossible that facilitation of sit-to-stand and walking train-ing was provided more consistently across sites in the facil-itated KT group than in the passive KT group. Results ofthe qualitative analysis also showed that both familiarityand agreement with recommended treatments becausethey are “clear and practical to follow” [34, 35] likelyhelped to promote their uptake [27]. Sit-to-stand andwalking training were implemented in at least 15% ofpatients in each group at baseline (unadjusted estimates)which suggests that some providers had the expertise toperform these treatments and considered them relevant.Sit-to-stand and walking training are also simple, task-oriented mobility treatments that are relevant to dailyliving. Complex treatments that either involve multiplesteps (UE constraint-induced therapy) or technology (func-tional electrical stimulation, treadmill training) were rarelyimplemented at baseline and demonstrated either nochange or reduced application post-intervention despitebeing supported by Level A evidence (i.e., evidence from atleast one randomized controlled trial, meta-analysis, orsystematic review). Based on the SCORE-IT qualitativefindings, this was likely because health professionals foundthat these treatments were time-consuming, and requiredspecial training or equipment that was not consistentlyavailable across sites [27]. Finally, asking nurses to applysit-to-stand and walking training, in addition to therapists,appeared an effective facilitated KT strategy as the percent-age of patients receiving sit-to-stand and walking practiceby nurses increased by 20% (vs a decrease of 11% in thecontrol group) and 8% (vs 3% in the control group), re-spectively. Improved team communication and interpro-fessional collaboration were noted as an unintendedoutcome of SCORE-IT [27]. The improved practice ofnurses, likely supported by the nurse facilitator in the facil-itated KT group, was particularly influential in the currentstudy as nurses provided a large proportion of the treat-ment data in the multivariable analysis.Standing balance training, which increased in the passiveKT group, is also a simple task-oriented treatment. Becauseproviders receiving facilitated KT were implementing stand-ing balance training at a high rate at baseline (68% for PTs),they may have prioritized improving adoption of othertreatments [13]. Results from the qualitative sub-study [27]indicate that a greater degree of facilitation of and/or agree-ment with the practicality of standing balance training inthe passive compared to the facilitated group, may help toexplain why this practice improved in the context of passivedissemination of the stroke rehabilitation guideline.Despite multiple strategies and resources, the facili-tated KT intervention was unsuccessful in improvingintegration of 18 treatments concurrently. The facilitatedapproach may not have adequately addressed barriers tointegrating numerous treatments simultaneously andcomplex treatments that were unfamiliar to providers.Targeting fewer treatments and providing individualhands-on training and access to an expert may be amore effective approach based on results from previousguideline implementation trials for low back pain [24]and whiplash [26] rehabilitation.LimitationsThe primary limitation of this process evaluation was theuse of self-report measures of practice that cannot captureclinical judgment or patient preferences and are vulner-able to over-reporting. This limitation, however, wouldhave affected both groups similarly. It could not be deter-mined if treatments received were appropriate due to theunavailability of clinical data at the time implementationwas evaluated. Results provide average rates of implemen-tation after controlling for patient and hospital character-istics to optimize comparability between groups.Salbach et al. Implementation Science  (2017) 12:100 Page 9 of 11ConclusionsA facilitated KT intervention incorporating a guidelinewith treatment protocols and trained local nurse and ther-apist facilitators was of limited effectiveness compared topassive guideline dissemination in improving short-termuptake of a comprehensive guideline by inpatient strokerehabilitation teams. Conducting this process evaluationas part of the trial was valuable as it revealed the nature ofthe practice change, in terms of the type of health pro-viders involved and the type of and extent to which treat-ments were implemented, underpinning patient functionoutcomes observed in the main analysis. The combinationof quantitative and qualitative process evaluation findingsprovided a basis for hypothesis generation related to de-signing KT interventions to overcome challenges to inte-grating treatments recommended in stroke rehabilitationguidelines in the context of interdisciplinary team care.Specifically, KT strategies that better address the need forstaff training and team functioning to account for treat-ment complexity and prioritization post-stroke may beneeded. Finally, the study design and analytical approachdescribed in the current study, which involved consider-ation of multi-level clustering effects, and adjustment forsite- and patient-level covariates, is innovative and willhelp to advance the field of implementation science in thecontext of rehabilitation guideline implementation.Additional fileAdditional file 1: Table S1. Checklist completion by provider group.Table S2. Intracluster correlation coefficients for the 18 recommendedtreatments. Table S3. Cluster sizes at the site, provider and patient levelby study group and sampling time point. Table S4. Unadjusted rate oftreatment implementation by healthcare professional and group pre- andpost-intervention. (DOCX 21 kb)AbbreviationsCI: Confidence Interval; CMSA: Chedoke McMaster Stroke Assessment;CONSORT: Consolidated Standards of Reporting Trials; FES: Functional ElectricalStimulation; FIM: Functional Independence Measure; ICC: Intracluster CorrelationCoefficient; KT: Knowledge Translation; KTA: Knowledge to Action; LE: LowerExtremity; OR: Odds Ratio; OT: Occupational Therapist; PARiHS: PromotingAction on Research Implementation; PT: Physical Therapist; RA: ResearchAssistant; RN: Registered Nurse; ROM: Range of Motion; SCORE-IT: StrokeCanada Optimization of Rehabilitation by Evidence-Implementation Trial;UE: Upper ExtremityAcknowledgementsIDG is a recipient of a Canadian Institutes of Health Research FoundationGrant (FDN #143237).FundingA grant from the Canadian Stroke Network was used to fund the conductof the study. The Toronto Rehabilitation Institute-University Health Networkprovided funding to support the analysis presented here. NMS wassupported by Ontario Ministry of Research and Innovation Early Researcherand Canadian Institutes of Health Research New Investigator Awards toconduct this research.Availability of data and materialsThe datasets generated during and/or analyzed during the current study arenot publicly available as participant consent and institutional approval forthis activity were not obtained.Authors’ contributionsMB, SWD, and IDG designed the study with input from the other co-authors.NMS planned the data analysis, and analyzed and interpreted the dataregarding providers’ implementation of 18 treatments recommended in thestroke rehabilitation guideline and drafted the manuscript. All authors readand approved the final manuscript.Ethics approval and consent to participateThe ethics board at each site and affiliated university approved the studyprotocol. Consent was obtained from site managers/physiatrists, and alltherapists and nurses working on each stroke rehabilitation unit.Consent for publicationNot applicable.Competing 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 details1Department of Physical Therapy, University of Toronto, 160-500 UniversityAve, Toronto, ON M5G 1V7, Canada. 2School of Physical and OccupationalTherapy, McGill University, 3630 Promenade Sir William Osler, Montreal, QCH3G 1Y5, Canada. 3Faculty of Medicine and Health Sciences, Université deSherbrooke, 3001, 12e avenue nord, Bureau FM-2208, Sherbrooke, QC J1H5N4, Canada. 4University of British Columbia, 212-2177 Wesbrook Mall,Vancouver, BC V6T 1Z3, Canada. 5School of Epidemiology and Public Health,University of Ottawa, 600 Peter Morand Cres, Ottawa K1G 5Z3, Canada.6School of Physiotherapy, Dalhousie University, Office 405 Forrest Building,5869 University Avenue, PO Box 15000, Halifax, NS B3H 4R2, Canada.7Division of Clinical Epidemiology, Division of Geriatrics, McGill UniversityHealth Center, Royal Victoria Hospital Site, Ross Pavilion R4.29, 687 Pine AveW, Montreal, QC H3A 1A1, Canada. 8Department of Rehabilitation, Faculty ofMedicine, Université Laval and Centre de recherche en réadaptation etintégration sociale (CIRRIS), Institut de réadaptation en déficience physiquede Québec (IRDPQ) Site Hamel, 525 Boul. Wilfrid-Hamel Est, Québec, QC G1M2S8, Canada. 9Parkwood Institute, 550 Wellington Road, London, ON N6C0A7, Canada. 10Schulich School of Medicine and Dentistry, WesternUniversity, Western Centre for Public Health and Family Medicine, 1151Richmond St, London, ON N6A 3K7, Canada. 11Toronto RehabilitationInstitute-University Health Network, 550 University Avenue, room 3-131(3-East) 3rd Floor University Wing, Toronto, ON M5G 2A2, Canada.Received: 31 October 2016 Accepted: 24 July 2017References1. Mayo NE, Wood-Dauphinee S, Cote R, Durcan L, Carlton J. Activity,participation, and quality of life 6 months poststroke. Arch Phys MedRehabil. 2002;83:1035–42.2. Krueger H, Koot J, Hall RE, O’Callaghan C, Bayley M, Corbett D. Prevalenceof individuals experiencing the effects of stroke in Canada: trends andprojections. Stroke. 2015;46:2226–31.3. 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Primaryhealth care professionals’ views on barriers and facilitators to theimplementation of the Ottawa decision support framework in practice.Patient Educ Couns. 2006;63:380–90.•  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:Salbach et al. Implementation Science  (2017) 12:100 Page 11 of 11


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