Motivational interviewing and university sport in canada: What do head coaches say?
Bibliographic record
Abstract
Objective: Grounded in Motivational Interviewing (MI; Miller & Rollnick, 2013), the aim of this exploratory study was to describe situations in sport where coaches report using MI when interacting with athletes. Methods: Using a non-experimental design, male (n = 91) and female (n = 27) head coaches representing twenty-one sports competing at the university-level across Canada reported their use of open-ended questions, affirmations, reflections, and summaries (OARS) with athletes in the following situations: (a) Designated practice sessions, (b) Competitive matches/games, (c) Team/Individual meetings on a 0 (never) to 4 (always) Likert type scale. Results: Coaches reported using affirmations (M = 2.91±0.65), open-ended questions (M = 3.08 ±0.72), summaries (M = 2.38± 0.83), and reflections (M = 2.04±0.83) across each situation. Combined overall average use of OARS was also evident across meetings (M = 2.82±0.54), practices (M = 2.45±0.53), and games (M = 2.20±0.58). Significant within-subject ANOVAS using Grenhouse-Geisser corrections indicated differences in use of each OARS skill, as well as differences in overall combined use of OARS across situations (both p values less than .05). Bonferroni pairwise comparison indicated that each individual OARS skill differed from the other, and combined use of OARS was different across each situation (all p values less than .05). Discussion: Coaches in this study reported communicating with athletes in ways germane to MI in various situations, which suggests that studying MI in relation to coach-athlete relations is a potential area of research that may strengthen our understanding of coach-athlete communication.Acknowledgments: Research funded by the Ontario Graduate Scholarship
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".