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Record W2037258865 · doi:10.1002/chp.15

Rehabilitation education program for stroke (REPS): Learning and practice outcomes

2005· article· en· W2037258865 on OpenAlexaff
Sara McEwen, Kristina Szurek, Helene J. Polatajko, Susan Rappolt

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2005
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRehabilitationIntervention (counseling)Medical educationStroke (engine)PsychologyMedicineBest practiceProgram evaluationNursingPhysical therapy

Abstract

fetched live from OpenAlex

INTRODUCTION: New research knowledge acquired from Web-based sources may have a better chance of being translated into practice when accompanied by additional educational strategies. This study was undertaken to investigate that hypothesis. METHODS: The Rehabilitation Education Program for Stroke (REPS) combines a self-directed online learning module with support from peer mentors, technical skills workshops, and organizational supports. Participants completed learning tests and practice surveys before and after the program and at a 6-month follow-up. RESULTS: Learning and self-reported practice outcomes improved in the areas of assessment, client-centered practice, support for family and caregivers, and detecting depression. Participants also identified and reported specific strategies for individual and programmatic practice change. DISCUSSION: A multifaceted, interdisciplinary online education intervention can positively influence stroke rehabilitation practices.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.465
Teacher spread0.444 · 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 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

Citations16
Published2005
Admission routes1
Has abstractyes

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