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Record W4221041415 · doi:10.1123/iscj.2020-0069

Coaches’ and Researchers’ Perceptions of the Factors That Influence Knowledge Translation for Coaching in Portugal and Brazil

2022· article· en· W4221041415 on OpenAlexaff
Fernando Santos, Marta Ferreira, Nicholas L. Holt

Bibliographic record

VenueInternational Sport Coaching Journal · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoachingKnowledge translationPortugueseThematic analysisKnowledge managementProcess (computing)ReflexivityPerceptionQuality (philosophy)PsychologyKnowledge creationMedical educationSociologyBusinessQualitative researchComputer scienceMarketingMedicineSocial science

Abstract

fetched live from OpenAlex

The main objective of this study was to examine Portuguese and Brazilian coaches’ and researchers’ perceptions of the factors that influence the knowledge translation processes in sport. Data were collected via focus groups with 36 coaches and 25 researchers from both countries. Results of a reflexive thematic analysis were captured by the themes: knowledge creation, barriers, knowledge dissemination and knowledge implementation, and evaluation. In both contexts, there was a gap between researchers’ and coaches’ needs, which hindered the knowledge translation process. The lack of partnerships in place between these stakeholders also created difficulties in the process of knowledge translation. Moving forward, universities and polytechnic institutes should forge meaningful partnerships with coaches so they can implement evidence-based practices and use research as a tool for improving the quality of the coaching process.

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)0.002
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 score0.006
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.106
GPT teacher head0.408
Teacher spread0.301 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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