Examining High School Teacher-Coaches’ Perspective on Relationship Building With Student-Athletes
Bibliographic record
Abstract
Adult leaders in sport can exert considerable influence on young athletes’ development but this influence is mediated by the quality of the relationship that is formed between both parties. The purpose of the current study was to examine high school teacher-coaches’ perspective on relationship building with student-athletes. Teacher-coaches (20 men, 5 women, Mage = 37.0 years, age range: 25–56 years) from Canada took part in semistructured interviews. Results indicated how the participants believed being both a teacher and a coach was advantageous because it allowed them to interact regularly with student-athletes. The teacher-coaches devised a number of strategies (e.g., early-season tournaments, regular team meetings) to nurture relationships and believed their recurrent interactions allowed them to exert a more positive influence on student-athletes than adult leaders in a single role. In terms of outcomes, the teacher-coaches believed their dual role helped increase their job satisfaction, positively influenced their identity, and allowed them to help student-athletes through critical family (e.g., alcoholism, divorce) and personal issues (e.g., suicide). The current study suggests that the dual role of teacher-coach is beneficial to both teacher-coaches and student-athletes. However, future work is needed, paying attention to how teacher-coaches can further nurture quality relationships with student-athletes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".