The impact of humanistic coach training on youth athletes’ development through sport
Bibliographic record
Abstract
The purpose of this study was to evaluate the impact of humanistic coach training on athletes’ developmental outcomes. A sample of 148 high school student-athletes between 12 and 17 years old from low socio-economic communities completed questionnaires during their season. The student-athletes were divided into an experimental group composed of student-athletes of 11 head coaches who participated in a humanistic coach training session, and a comparison group composed of student-athletes of 8 untrained coaches. Four questionnaires were used to assess competence, confidence, connection to the coach, and character (prosocial and antisocial behaviours) of the participants. The effects of the humanistic coach training program were assessed using repeated measures analysis of variance models. Results showed that connection to the coach worsened for athletes of untrained coaches. Also, participants from both groups reported an increase in antisocial behaviours at the end of the season, but the athletes of trained coaches reported engaging less frequently in antisocial behaviours compared to athletes of untrained coaches. These findings suggest that teaching humanistic coaching may help practitioners foster positive developmental outcomes in youth sport participants and build positive coach-athlete relationships, while also raising awareness to the use of sport as a tool to promote personal growth and development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".