Developing Elite Strength and Conditioning Coaches’ Practice Through Facilitated Reflection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".