The Game Plan: A Scoping Review of Implementation Science in Sport Science
Bibliographic record
Abstract
Integrating evidence-informed information into everyday practice can present significant challenges. The field of implementation science (IS) emerged to help improve the connection of scientific findings to standard practice and minimize the knowledge-to-action gap. IS refers to the scientific study of methods to promote the adoption of evidence-informed practices (EIP) and has been adopted in many disciplines, including sport sciences. The present scoping review provides a comprehensive and critical overview of how IS has been used in the context of sport science between 1900 and 2024. Four databases (i.e., Web of Science, Sport Discus, Scopus, Medline-OVID) were searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The search yielded 31 articles that met the final inclusion criteria, and study characteristics were extracted and assessed including the article’s methods, focus/foci, tools, strategies, and outcomes. Results revealed that most (over 50%) of the articles meeting the criteria examined how IS can be used for injury prevention, with very few articles examining how IS can be used for other areas of sport including psychological interventions or coaching practices (amongst others). Importantly, in studies where IS was integrated into the methodological design, there was a greater efficiency in resource allocation and health outcomes for end-users. This scoping review provides important insights about the landscape of IS in sport science and highlights opportunities for researchers and practitioners to employ IS methods for enhancing the adoption of EIPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.179 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".