What the Empirical Studies on Sport Coach Education Programs in Higher Education Have to Reveal: A Review
Bibliographic record
Abstract
Facing the increase in the number of publications, often spread across many journals, researchers have developed different approaches to review the literature. In the first part of this article we present the result of an “overview” type of review of literature on sport coach education programs in higher education (HE) between 2000–2018. By sharing the list of the 38 articles found, along with some general characteristics of these, our hope is, as suggested in many reviews of literature, that researchers will use the review to more easily frame their studies and discuss their results. However, there are very few examples of how this is done. We would argue that how researchers use the same review of literature varies depending of their context, research interests, and research paradigm. Therefore, in the second part of the article we highlight what we took from our review of the literature based on our specific research context—Brazilian HE sport coach education programs. The three key topics presented and discussed are: (a) the importance of considering the student-coaches’ biography, (b) how to prepare student-coaches for reflective practice, and (c) the complexity of internships.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".