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Record W2090034893 · doi:10.3109/09638288.2012.656790

Barriers to implementation of stroke rehabilitation evidence: findings from a multi-site pilot project

2012· article· en· W2090034893 on OpenAlexaffabout
Mark Bayley, Amanda Hurdowar, Carol L. Richards, Nicol Korner‐Bitensky, Sharon Wood-Dauphinée, Janice J. Eng, Marilyn McKay-Lyons, Edward Harrison, Robert Teasell, Margaret B. Harrison, Ian D. Graham

Bibliographic record

VenueDisability and Rehabilitation · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaQueen's UniversityWestern UniversityHospital for Sick ChildrenUniversity of British ColumbiaMcGill UniversityDalhousie UniversityUniversity of TorontoCanadian Institutes of Health ResearchToronto Rehabilitation Institute
Fundersnot available
KeywordsStaffingRehabilitationMedicineFacilitatorFocus groupNursingEvidence-based practiceGuidelinePrioritizationOccupational therapyPhysical therapyPsychologyProcess managementAlternative medicine

Abstract

fetched live from OpenAlex

Purpose: To describe the barriers to implementation of evidence-based recommendations (EBRs) for stroke rehabilitation experienced by nurses, occupational therapists, physical therapists, physicians and hospital managers. Methods: The Stroke Canada Optimization of Rehabilitation by Evidence project developed EBRs for arm and leg rehabilitation after stroke. Five Canadian stroke inpatient rehabilitation centers participated in a pilot implementation study. At each site, a clinician was identified as the “local facilitator” to promote the 6-month implementation. A research coordinator observed the process. Focus groups done at completion were analyzed thematically for barriers by two raters. Results: A total of 79 rehabilitation professionals (23 occupational therapists, 17 physical therapists, 23 nurses and 16 directors/managers) participated in 21 focus groups of three to six participants each. The most commonly noted barrier to implementation was lack of time followed by staffing issues, training/education, therapy selection and prioritization, equipment availability and team functioning/communication. There was variation in perceptions of barriers across stakeholders. Nurses noted more training and staffing issues and managers perceived fewer barriers than frontline clinicians. Conclusions: Rehabilitation guideline developers should prioritize evidence for implementation and employ user-friendly language. Guideline implementation strategies must be extremely time efficient. Organizational approaches may be required to overcome the barriers.Implications for RehabiliationDespite increasingly strong evidence for stroke rehabilitation, there are delays in implementation into clinical practice.This study showed that lack of time, staffing issues, staff education, therapy selection or prioritization, lack of equipment and team functioning were the main barriers to implementation.Managers and stakeholders should consider these barriers and prioritize evidence when implementing.

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)0.068
metaresearch head score (Gemma)0.123
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.123
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.003
Scholarly communication0.0030.003
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.329
GPT teacher head0.613
Teacher spread0.284 · 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 designQualitative
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

Citations192
Published2012
Admission routes2
Has abstractyes

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