Enhancing impact: A systematic review of implementation science in sport psychology research
Bibliographic record
Abstract
Significant challenges often exist when incorporating scientific evidence into practice. In some research domains, it can take roughly 17 years to incorporate evidence-based practices (EBPs) into routine practice (Balas & Boren, 2000; Grant et al., 2003; Morris et al., 2011). One of the strategies for helping reduce barriers and facilitate the adoption of EBP into “routine” practice is through implementation science practices (IS; Eccles & Mittman, 2006). The aim of this review was to provide a comprehensive and critical overview of IS in sport psychology research. A systematic review of four databases (Web of Science, Sport Discus, Scopus, Medline-OVID) from 1900 to 2022 was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; Moher et al., 2009). Full-text articles including relevant data on IS in sport were identified. The initial search revealed 645 articles and after screening, 33 articles remained for final review. Articles were assessed for quality using a 3-point Likert-type scale based on clarity of research aim and components of IS. The most prevalent number of articles focused on injury prevention programs (42%), and 48% had male and female samples. Of these articles, only one sport psychology article explicitly used a component of IS. Although, 12% studied wellness and performance interventions with athletes, they did not explicitly utilize IS. This systematic review revealed important findings about the landscape of IS in sport psychology research and highlights several opportunities for researchers and practitioners to utilize IS to enhance the implementation of research findings.
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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 | 0.088 | 0.259 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.035 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".