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Record W3208099259 · doi:10.1177/17479541221122435

Assessing the effectiveness of the transformational coaching workshop using behavior change theory

2022· article· en· W3208099259 on OpenAlexafffund
Caroline Hummell, Jordan D. Herbison, Jennifer Turnnidge, Jean Côté

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsQueen's UniversityMcGill UniversityBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCoachingTransformational leadershipIntervention (counseling)PsychologyApplied psychologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

The current study assessed how participation in the Transformational Coaching Workshop (TCW) influenced youth sport coaches’ perceived capability, opportunity, and motivation to incorporate transformational coaching behaviors into their coaching practices. Sixty-three volunteer youth sport coaches participated in the study as part of an intervention ( n = 31; M age = 45.65 years; SD age = 8.82 years) or comparison group ( n = 32; M age = 44.59 years; SD age = 11.86 years). The study employed a two-arm, pre- and post-intervention, non-randomized intervention design. Dependent- and independent-sample t-tests were conducted to assess within and between-group differences. Results indicated that participants in the intervention group reported slight improvements in their perceived capability and opportunity to use transformational coaching behaviors post-intervention. There were no significant differences between groups post-intervention. This study provides support for the effectiveness of the TCW, and the application of behavior change frameworks to evaluate coach development programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.387
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2022
Admission routes2
Has abstractyes

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