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Record W2752988752 · doi:10.1177/1747954117730987

Creation, implementation, and evaluation of a values-based training program for sport coaches and physical education teachers in Singapore

2017· article· en· W2752988752 on OpenAlexaff
Koon Teck Koh, Martin Camiré, Gordon A. Bloom, John Wang

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsCoachingPhysical educationPsychologyThematic analysisAthletesMedical educationTraining (meteorology)Athletic trainingPedagogyMathematics educationPhysical therapyQualitative researchMedicineSociology

Abstract

fetched live from OpenAlex

The purpose of the study was to create, implement, and evaluate a values-based training program for sport coaches and physical education teachers in Singapore. Four sport coaches with two to eight years of coaching experience ( M = 5.0, SD = 1.4) and four physical education teachers with two to nine years of teaching experience ( M = 5.25, SD = 3.3) took part in three 2-h training workshops focusing on the teaching of values to athletes and students. Following the workshops, the participants took part in an individual interview to document their perspectives of the values-based training program. Results of the thematic analysis revealed many benefits of the values-based training program, including an increased awareness of the importance of systematically teaching values through physical education and sport. Further, the participants felt better equipped to work with parents and other teachers in helping athletes and students transfer values.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.465
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.167
GPT teacher head0.594
Teacher spread0.426 · 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

Citations36
Published2017
Admission routes1
Has abstractyes

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