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Record W2739942871 · doi:10.1186/s13012-017-0631-7

Facilitated interprofessional implementation of a physical rehabilitation guideline for stroke in inpatient settings: process evaluation of a cluster randomized trial

2017· article· en· W2739942871 on OpenAlexafffundabout
Nancy M. Salbach, Sharon Wood-Dauphinée, Johanne Desrosiers, Janice J. Eng, Ian D. Graham, Susan Jaglal, Nicol Korner‐Bitensky, Marilyn MacKay-Lyons, Nancy E. Mayo, Carol L. Richards, Robert Teasell, Merrick Zwarenstein, Mark Bayley

Bibliographic record

VenueImplementation Science · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité LavalParkwood InstituteCentre for Interdisciplinary Research in RehabilitationRoyal Victoria Regional Health CentreMcGill UniversityUniversity of OttawaUniversity of British ColumbiaWestern UniversityUniversity of TorontoUniversité de SherbrookeToronto Rehabilitation InstituteRoyal Victoria HospitalDalhousie UniversityUniversity Health Network
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchToronto Rehabilitation InstituteCanadian Stroke Network
KeywordsMedicineHealth services researchRandomized controlled trialRehabilitationHealth administrationGuidelineHealth informaticsStroke (engine)Cluster randomised controlled trialCluster (spacecraft)Physical therapyPublic healthPhysical medicine and rehabilitationMedical emergencyNursingSurgeryPathologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: 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 guideline was 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 designed to assess whether the type and number of recommended treatments implemented by stroke teams in each group would 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 allocated to a facilitated or passive KT intervention group. Sites in the facilitated group received the guideline with treatment protocols and funding for a part-time nurse and therapist facilitator who attended a 2-day training workshop and promoted guideline implementation for 16 months. Sites in the passive group received the guideline excluding treatment protocols. As part of a process evaluation, nurses, and occupational and physical therapists, blinded to study hypotheses, 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 was compared after adjusting for clustering and covariates in a random-effects logistic regression analysis. 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 implementation of sit-to-stand (p = 0.028) and walking (p = 0.043) training while the passive KT intervention was associated with improved implementation of standing balance training (p = 0.037), after adjusting for clustering at patient and provider levels and covariates. CONCLUSIONS: Despite multiple strategies and resources, the facilitated KT intervention was unsuccessful in improving integration of 18 treatments concurrently. The facilitated approach may not have adequately addressed barriers to integrating numerous treatments simultaneously and complex treatments that were unfamiliar to providers. TRIAL REGISTRATION: Unique identifier- NCT00359593.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.054
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.527
Teacher spread0.469 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations51
Published2017
Admission routes3
Has abstractyes

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