Intercollegiate coaches' experiences and strategies for coaching first-year athletes
Bibliographic record
Abstract
University student-athletes have reported difficulties balancing the rigors of academics, athletics, and their personal lives (Heller, Bloom, Neil, & Salmela, 2005). These challenges may be exacerbated for first-year athletes who are transitioning from high school and often living away from home for the first time. Given that coaches significantly influence their athletes’ experiences (Bloom, Falcão, & Caron, 2014), their coaching styles and support may ease this transition process. Thus, the purpose of the current study was to investigate university coaches’ perceptions, experiences, and strategies used with first-year university student-athletes. Eight highly successful and experienced Canadian university coaches were individually interviewed. The interview data was analyzed with thematic analysis (Braun & Clarke, 2006). The results revealed that coaches’ ultimate goal of helping their athletes succeed in life after university influenced their coaching practices throughout the athletes’ entire university experience from first year to graduation. Coaching first-year athletes started with recruiting individuals who were highly skilled and who would fit in with the other athletes on their team both on and off the field. Once selected, coaches began building trusting relationships with their first-year athletes that were supported by the leadership skills of their senior athletes and by incorporating team building activities into training. Coaches developed their first-year athletes’ athletic skills by creating training programs that addressed each players’ weaknesses. At the same time, coaches ensured their academic success by monitoring their progress and encouraging the use of academic resources that were available from their university. The coaches also attended to their athletes’ personal needs which resulted from coaching individuals who were living away from home for the first time and who were susceptible to social opportunities that could distract them from their academic and athletic responsibilities if not carefully monitored by the coaches. In sum, this study adds to the body of literature of effective coaching practices for university coaches by providing one of the first empirical accounts of coaching first-year student-athletes. 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 strategies that can help resolve such challenges and ease this transition process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".