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Record W2281181230 · doi:10.1260/1747-9541.10.6.1055

Knowledge Translation of Sport Psychology to Coaches: Coaches' Use of Online Resources

2015· article· en· W2281181230 on OpenAlexaff
J. Paige Pope, Nicole Westlund Stewart, Barbi Law, Craig Hall, Melanie Gregg, Rebecca Robertson

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2015
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsNipissing UniversityWestern UniversityUniversity of WinnipegUniversity of Ottawa
Fundersnot available
KeywordsSport psychologyCoachingPsychologyAthletesApplied psychologyCertificationCompetitive sportMedical educationManagementPhysical therapy

Abstract

fetched live from OpenAlex

Knowledge translation is an essential component of the research process. The purpose of this study was to examine the content of the information coaches attain from online sport psychology resources and their use of this information. This study also investigated differences in coaches' use of online resources across experience, certification, and competitive level of the coaches. Participants included 253 ( n males = 183; n females = 69) coaches averaging 13.6 years of coaching experience who varied considerably in the sport, income level, competitive level, and age of the athletes they coached. Results demonstrated that coaches currently get information from online sport psychology resources “a few times per year”, but would get it “once per month” if more accessible and credible resources were available. The study findings also indicated that coaches primarily get general information related to sport psychology from online resources, but would be interested in applied information such as sport psychology tips, skills/strategies, and how to implement sport psychology with their athletes.

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)0.003
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 score0.064
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.181
GPT teacher head0.434
Teacher spread0.253 · 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

Citations43
Published2015
Admission routes1
Has abstractyes

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