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Record W4410375803 · doi:10.3390/educsci15050603

Developing Elite Strength and Conditioning Coaches’ Practice Through Facilitated Reflection

2025· article· en· W4410375803 on OpenAlexaff
Christoph Szedlak, Bettina Callary, Matthew J. Smith

Bibliographic record

VenueEducation Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsCape Breton University
Fundersnot available
KeywordsEliteReflection (computer programming)ConditioningPsychologyMathematics educationPedagogyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Recent research has suggested that strength and conditioning (S&C) coach development should consider constructivist learning theories to promote coach development and learning of psychosocial coaching competencies. Reflective practice can encourage holistic learning through promoting an internal dialogue of the meaningfulness of an individual’s experiences. Our study aimed to examine the efficacy of a facilitated, guided, and longitudinal reflective process to promote coach learning of psychosocial coaching practice using Moon’s reflective framework. Over a four-week period, six elite S&C coaches engaged in a guided process reflection process with a facilitator. This included daily journaling in an e-diary with the facilitator providing feedback at the end of each week. At the end, each S&C coach participated in an exit interview. Data were analysed using interpretative phenomenological analysis. Findings revealed that there were potential benefits for the S&C coach’s process of reflection such as providing accountability through developing a close relationship with the facilitator, which enabled the S&C coaches to more critically link learning to behaviour change. Furthermore, S&C coaches’ learning resulted in developing awareness of self/athlete’s needs, increased flexibility, and enhanced confidence. This resulted in S&C coaches developing psychosocial coaching competencies that enabled them to change their practice to become more athlete centred.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.098
GPT teacher head0.486
Teacher spread0.388 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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