A Guide to Conducting Systematic Reviews of Coaching Science Research
Bibliographic record
Abstract
Research in coaching science continues to grow and as such, there is a need for rigorous tools to help make sense of the rapidly expanding literature. The purpose of this paper is to provide a detailed description of a systematic review methodology that can be used to summarise literature in coaching science. To do so, we present a test case of a systematic review we conducted on the sport coaching experiences of global Indigenous populations. More precisely, we conducted a systematic review of English, Spanish, French, Mandarin, and Portuguese peer-reviewed journal articles, spanning twelve databases (e.g., Sport Discus, ERIC, and Scopus) from 1970 to 2014. ENTREQ and COREQ guidelines were followed to report the results of the systematic review, and Bronfenbrenner’s ecological systems theory was used as a theoretical framework to extract and synthesise relevant findings from the included articles. In sum, this paper presents a robust methodology for systematically reviewing research in coaching science and provides practical insights for those who endeavour to conduct rigorous literature searches in this domain.
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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 | 0.285 | 0.395 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.035 | 0.038 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.065 | 0.027 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".