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Record W3093134577 · doi:10.1177/1747954120964075

Follow-up evaluation of the Coaching for Life Skills online training program

2020· article· en· W3093134577 on OpenAlexafffund
Stéphanie Turgeon, Martin Camiré, Scott Rathwell

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of OttawaUniversity of LethbridgeUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCoachingPsychologyCLs upper limitsInterpersonal communicationIntervention (counseling)AthletesLife skillsMultiple baseline designMedical educationPhysical therapyApplied psychologyMedicinePedagogySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Coach education has been positioned as an important catalyst in enabling coaches to maximise the positive influence of high school sport on student-athlete development. The purpose of the study was to conduct a subsequent season follow-up evaluation, examining longer-term changes in coach-athlete relationship, coach interpersonal behaviours, and life skills teaching in coaches who completed the Coaching for Life Skills (CLS) online training program. A 2 × 2 prospective causal comparative design was used. Coaches ( n = 285) were asked to complete follow-up measures during the high school sport season that followed their completion of the trial. The response rate was 36.84%, with the final sample consisting of 64 participants. Data were analysed using independent sample t -tests. From baseline to subsequent season follow-up, scores for coach-athlete relationship and coach interpersonal behaviours significantly increased for the CLS group and significantly decreased for the comparison group. No significant differences in scores were found on life skills teaching from baseline to subsequent season follow-up. The results suggest that the intervention may have helped CLS group coaches remain consistent in their use of relational coaching behaviours.

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)0.009
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.102
GPT teacher head0.404
Teacher spread0.302 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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