Coaches’ Experiences Learning and Applying the Content of a Humanistic Coaching Workshop in Youth Sport Settings
Bibliographic record
Abstract
The purpose of this study was to develop and deliver a humanistic coaching workshop, as well as investigate coaches’ perceptions of this workshop and their experiences using humanistic coaching. Participants were 12 coaches of grade 7–11 basketball teams from schools in low socioeconomic communities in a major Canadian city. Data were collected using semistructured interviews and personal journals. An inductive thematic analysis revealed coaches perceived the workshop to be effective in teaching the humanistic principles and how to apply them in youth sport settings. The perceived strengths of the workshop included the group discussions, use of videos, practical coaching examples, and learning about the findings from empirical studies. The participants applied the humanistic principles with their teams by asking questions that guided athlete learning and by requesting feedback about various individual and team matters. Despite facing challenges such as increased time and effort to implement humanistic coaching principles, the participants reported positive outcomes in their athletes related to autonomy, communication, motivation, and willingness to help teammates. These results are discussed using literature on youth sport coaching, knowledge translation, and youth development through sport. Findings from this study can be used to enhance youth sport coach training protocols.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".