Empirical Support for the Tenets of Sport Participation and Physical Activity-Based Models: A Scoping Review
Bibliographic record
Abstract
Notwithstanding their wide-spread use, it is unclear what level of empirical evidence exists to support sport participation and physical activity-based models. Sport participation and physical activity-based models characterize different stages of sport involvement based on sport activities (organized and unorganized) individuals take part in throughout their lifespan. The objectives of this scoping review was to explore the nature of empirical support for tenets of sport participation and physical activity-based models describing the evolution of an individuals' sport participation. Seventeen different sport participation models were identified through an iterative literature review, using a snowball search strategy and expert (n= 8) consultation. Of the identified models, three described the evolution of an individual's sport participation based on their participation in different activities at various stages of sport involvement and were retained for the review. A second literature review identified peer-reviewed publications supporting at least one tenet of these three models. Many tenets of retained models received some empirical support from some of the 38 publications identified, but some tenets were not tested. Most of the evidence supporting tenets originated from studies among elite-level athletes. Whereas some evidence exists to support current sport participation and physical activity models, more research is warranted, particularly among the general population of non-elite athletes, for the models to be used in full confidence to guide sport policies, programs, and practices.
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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 | 0.046 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".