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Record W4213112258 · doi:10.1093/ptj/pzac014

Factors That Influence the Clinical Implementation of Aerobic Exercise in Stroke Rehabilitation: A Theory-Informed Qualitative Study

2022· article· en· W4213112258 on OpenAlexafffundabout
Elizabeth L. Inness, David Jagroop, Angie Andreoli, Mark Bayley, Louis Biasin, Cynthia J. Danells, J Hall, Avril Mansfield, Alison McDonald, Diane Nishri, Nancy M. Salbach, Denise Taylor, Karl Wong, Ada Tang

Bibliographic record

VenuePhysical Therapy · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster UniversityNOSM UniversitySt. Joseph's Care GroupNova Scotia Cancer CentreToronto Rehabilitation InstituteSunnybrook Health Science CentreHamilton Health SciencesUniversity of TorontoUniversity Health Network
FundersUniversity of TorontoToronto Rehabilitation InstituteHeart and Stroke Foundation of Canada
KeywordsRehabilitationStroke (engine)Physical medicine and rehabilitationPhysical therapyAerobic exerciseQualitative researchPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: Best practice guidelines recommend that aerobic exercise (AEx) be implemented as early as possible poststroke, yet the prescription of AEx remains limited in stroke rehabilitation settings. This study used theoretical frameworks to obtain an in-depth understanding of barriers and enablers to AEx implementation in the stroke rehabilitation setting. METHODS: A qualitative, descriptive study was conducted. Participants were recruited from 4 stroke rehabilitation settings in Ontario, Canada, that have participated in an implementation study to provide structured AEx programming as part of standard care. Six clinician focus groups (with 19 physical therapists and 5 rehabilitation assistants) and one-to-one interviews with 7 managers and 1 physician were conducted to explore barriers and enablers to AEx implementation. The Theoretical Domains Framework and Consolidated Framework for Implementation Research informed content analysis for clinician and manager perspectives, respectively. RESULTS: Barriers specific to resource constraints and health care system pressures, combined with patient goals, led to interventions to improve function being prioritized over AEx. Successful implementation was enabled through an interprofessional approach and team engagement in the planning and implementation process. Health care providers described concerns about patient safety, but confidence and capability for implementing AEx were enabled by education, skill development, use of exercise tests, and consultation with individuals with content expertise. Participants described the development of supportive processes that enabled AEx implementation within team workflows and shared resources. CONCLUSION: Strategies to support implementation of AEx in stroke rehabilitation should incorporate knowledge and skills, the provision of clinical decision-making tools, access to expert consultation, the roles and social influence of the interprofessional team and formal and informal leaders, and supportive processes adapted to the local context. IMPACT: Results from this study will inform the development of a clinical implementation toolkit to support clinical uptake of AEx in the stroke rehabilitation setting.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.206
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.545
GPT teacher head0.706
Teacher spread0.162 · 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 teacher head, 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

Citations16
Published2022
Admission routes3
Has abstractyes

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