A Guided Reflection Intervention for High Performance Basketball Coaches
Bibliographic record
Abstract
The purpose of this study was to conduct a guided reflection intervention for high performance basketball coaches. The study participants included two head basketball coaches and 10 of their players who were part of elite youth teams in Singapore. The coaches were highly experienced, each with 17 and 20 years of coaching experience respectively, and the players from both teams (one male and one female) reported on average three years of playing experience at the national youth level. The Singapore coaching behavior scale for sport (CBS-S basketball), on-site observations, and interviews were used to gather data from the coaches and players. Coaches also kept a reflective journal throughout the intervention. The results showed how the coaches responded differently to the guided reflection intervention (implemented by the first author) in terms of their willingness to adapt and integrate new perspectives into their coaching practice. The coaches’ level of reflection was found to be contingent upon a) their motivation and desire to be engaged in the process and b) the worth they saw in the learning facilitator’s recommendations to improve their athletes’ technical and tactical development. The results also showed how the coaches’ behaviors were linked to players’ satisfaction level with their work. The results are discussed using the coaching science literature and practical implications are proposed to optimize coaches’ use of reflection as a learning tool to improve their coaching practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".