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Record W2766718880 · doi:10.1080/21520704.2017.1388893

PYDSportNET: A knowledge translation project bridging gaps between research and practice in youth sport

2017· article· en· W2766718880 on OpenAlexaff
Nicholas L. Holt, Martin Camiré, Katherine A. Tamminen, Kurtis Pankow, Shannon R. Pynn, Leisha Strachan, Dany J. MacDonald, Jessica Fraser‐Thomas

Bibliographic record

VenueJournal of Sport Psychology in Action · 2017
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsYork UniversityUniversity of Prince Edward IslandUniversity of ManitobaUniversity of TorontoUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsKnowledge translationBridging (networking)Knowledge managementPsychologyKnowledge creationKey (lock)Action (physics)BusinessComputer science

Abstract

fetched live from OpenAlex

Generating a common understanding of knowledge translation among stakeholders is a key issue for increasing the use of research evidence in practice. The purpose of this article is to create a better understanding of knowledge translation in youth sport by providing a framework and guidelines for facilitating knowledge translation. We present PYDSportNET, a knowledge translation project designed to enhance the use of research evidence to promote positive youth development (PYD) through sport. This project is guided by the Knowledge to Action framework, which has two components (knowledge creation and the action cycle). For the knowledge creation component, we completed a meta-synthesis and created knowledge products. For the action cycle, we completed two studies with key sport stakeholders. Simultaneously, we created a knowledge dissemination and exchange network. Having described these activities, we go on to highlight some lessons learned to date and next steps for the project.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.065
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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 head0.342
GPT teacher head0.529
Teacher spread0.186 · 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 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
Published2017
Admission routes1
Has abstractyes

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