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Record W3014748410 · doi:10.1177/1526924820913506

Disseminating Knowledge to Providers on Exercise Training After Solid Organ Transplantation

2020· article· en· W3014748410 on OpenAlexafffundabout
Tania Janaudis‐Ferreira, Sunita Mathur, Catherine M. Tansey, Tom Blydt‐Hansen, David Hartell

Bibliographic record

VenueProgress in Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaMcGill UniversityTranslational Research in OncologyMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsDisseminationKnowledge translationRehabilitationMedicineTransplantationBest practiceMedical educationHealth careHealth professionalsIdentification (biology)Information DisseminationTarget audienceNursingKnowledge managementPhysical therapyComputer scienceBusiness

Abstract

fetched live from OpenAlex

INTRODUCTION: The objectives of our dissemination project were (1) to disseminate the evidence supporting exercise training in solid organ transplantation to exercise professionals, health-care professionals, physicians, and directors of transplant programs in order to enhance their ability to apply evidence to practice and (2) to build a community of exercise professionals and researchers across Canada. METHODS: We used the 5-step Patient-Centered Outcomes Research Institute model for knowledge translation to guide our project: (1) evidence assessment, (2) audience and partner identification, (3) dissemination, (4) implementation, and (5) evaluation. After meeting with experts in the field, conducting a literature review, and identifying an appropriate audience, we took our presentations on the road across Canada. RESULTS: We visited 10 transplant centers and held interactive knowledge translation sessions in each center. To provide sustainability and to facilitate the adoption of the research evidence, we founded the Canadian Network for Rehabilitation and Exercise for Solid Organ Transplant Optimal Recovery network and created its website. CONCLUSIONS: Our project raised awareness of the importance of exercise among many health professionals in Canada and built a community of exercise professionals and researchers in the field of transplantation through the rehabilitation network. It also led to the creation of online resources that will facilitate the implementation of rehabilitation programs in transplant centers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.366
Teacher spread0.325 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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