360-Degree Feedback for Sport Coaches: A Follow-Up to O’Boyle (2014)
Bibliographic record
Abstract
The sport literature yields little information concerning the available methods or processes coaches can use to obtain feedback about their coaching. This is unfortunate given that evaluative feedback about one’s coaching performance is useful in terms of providing direction for professional coach development (Mallett & Côté, 2006). As a follow-up to O’Boyle (2014), the purpose of this Best Practices paper is to offer a sample protocol for employing a 360-degree feedback system for coaches working in high performance settings. We draw on a review of the coach evaluation and 360-degree feedback literature, along with insights shared from Canadian intercollegiate head coaches to highlight some of the potential benefits and challenges of implementing a 360-degree feedback system in sport. We then suggest ‘best practices’ for effectively integrating this appraisal system and provide an example coach report to illustrate how feedback would be provided to a coach following a 360-degree feedback protocol. It is our hope that this sample protocol paper will encourage coaches, athletic directors, and other sport administrators to integrate comprehensive coach feedback practices in their sporting programs.
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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 | 0.074 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".