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Record W2979052510 · doi:10.1177/1747493019873597

Moving stroke rehabilitation research evidence into clinical practice: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable

2019· article· en· W2979052510 on OpenAlexafffund
Janice J. Eng, Marie‐Louise Bird, Erin Godecke, Tammy Hoffmann, Carole Laurin, Olumide Ayoola Olaoye, John M. Solomon, Robert Teasell, Caroline Watkins, Marion Walker

Bibliographic record

VenueInternational Journal of Stroke · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsParkwood InstituteLawson Health Research InstituteWestern UniversityUniversity of British Columbia
FundersNational Health and Medical Research CouncilCanadian Institutes of Health ResearchIpsenNational Institute for Health and Care Research
KeywordsStaffingMedicineKnowledge translationRehabilitationService delivery frameworkBest practiceHealth careNursingService providerEvidence-based practiceStroke (engine)Service (business)Medical educationAlternative medicineKnowledge managementPhysical therapyBusiness

Abstract

fetched live from OpenAlex

Moving research evidence to practice can take years, if not decades, which denies stroke patients and families from receiving the best care. We present the results of an international consensus process prioritizing what research evidence to implement into stroke rehabilitation practice to have maximal impact. An international 10-member Knowledge Translation Working Group collaborated over a six-month period via videoconferences and a two-day face-to-face meeting. The process was informed from surveys received from 112 consumers/family members and 502 health care providers in over 28 countries, as well as from an international advisory of 20 representatives from 13 countries. From this consensus process, five of the nine identified priorities relate to service delivery (interdisciplinary care, screening and assessment, clinical practice guidelines, intensity, family support) and are generally feasible to implement or improve upon today. Readily available website resources are identified to help health care providers harness the necessary means to implement existing knowledge and solutions to improve service delivery. The remaining four priorities relate to system issues (access to services, transitions in care) and resources (equipment/technology, staffing) and are acknowledged to be more difficult to implement. We recommend that health care providers, managers, and organizations determine whether the priorities we identified are gaps in their local practice, and if so, consider implementation solutions to address them to improve the quality of lives of people living with stroke.

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)0.056
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.468
Teacher spread0.376 · 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.

Study designObservational
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

Citations46
Published2019
Admission routes2
Has abstractyes

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