Intercollegiate coaches’ experiences and strategies for coaching first-year athletes<sup>*</sup>
Bibliographic record
Abstract
University student-athletes have reported difficulties balancing the rigours of academic study, athletics, and their personal lives. These challenges may be exacerbated for first-year athletes who are transitioning from secondary school into university. Given that coaches significantly influence their athletes’ experiences, their coaching styles and support may ease this transition process. Thus, the purpose of the current study was to investigate university coaches’ experiences and strategies used with first-year student-athletes. Eight highly successful and experienced university coaches of men’s team sports participated in individual semi-structured interviews. A thematic analysis revealed that coaches created a supportive team environment for first-year athletes by building trusting relationships with them, showing patience with their development, and encouraging leadership from senior athletes. To further facilitate first-year athletes’ success in and out of sport, coaches helped them accept their role on the team and improve their physical conditioning. Coaches also monitored their academic progress and advocated the use of available university resources such as tutors and support programmes. The current results benefit both coaches and athletes by highlighting the common challenges of a first-year university athlete, as well as by offering useful coaching strategies that can help this transition.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".