Scoping review protocol of sport research partnership literature: Identifying opportunities, challenges, and areas of need
Bibliographic record
Abstract
Background. Research-practice gaps in sport hinder the full potential of informing, developing, and optimizing sport settings for impact (e.g., across youth development, coaching, health promotion; Holt et al., 2018). Research partnerships between researchers and knowledge users offer a promising approach to bridge these gaps through knowledge translation/mobilization processes (i.e., shared decision-making, knowledge exchange, co-producing products). However, barriers often impede meaningful engagement within these partnerships (e.g., incompatible values/beliefs, power imbalances; Peachey & Cohen, 2016). A comprehensive review of sport research partnership literature can provide guidance on how to better support and implement these partnerships. Purpose. Given recent calls for greater research transparency, this presentation will provide an a priori scoping review protocol to identify opportunities, challenges, and areas of need to promote meaningful sport research partnerships. Methods. The protocol is informed by Arksey & O’Malley's (2005) framework, Peters et al.’s (2022) reporting guidelines, previous reviews on partnerships (Hoekstra et al., 2020, 2022), and input from a steering group. Four databases (SPORTDiscus, PsychINFO, ERIC, Web of Science) will be systematically searched, followed by a two-phase screening process based on pre-defined inclusion/exclusion criteria. Data extraction will be guided by the knowledge-to-action cycle (Graham et al., 2006) allowing mapping of extracted data to key phases of knowledge translation/mobilization. Implications. This work will significantly contribute to sport science by addressing the limited knowledge on knowledge translation/mobilization and partnership guidance within this context. Practically, the study findings will enhance clarity on how to support meaningful sport partnerships and bridge research-practice gaps.
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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 | 0.215 | 0.255 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.100 | 0.031 |
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".