Toddler and preschooler sport participation: Take-up, pathways, and patterns of engagement
Bibliographic record
Abstract
While prominent life-span sport participation and development models suggest children enter organized sport at approximately six years of age (i.e., Developmental Model of Sport Participation [DMSP], CA´tA© & Fraser-Thomas, 2016; Long-Term Athlete Development Model [LTAD], CS4L, 2016), current trends indicate active free play is being replaced by organized sport at markedly younger ages (AAP, 2019; ParticipACTION, 2018). As limited research captures children's sport experiences during the early years (i.e., ages 0-6), this study aimed to advance understanding of toddler and preschooler sport participation, by exploring patterns of sport take-up, pathways, and general patterns of engagement. Data were collected through external program observations of five early-years sport programs (i.e., multi-sport, soccer, hockey, rugby, and gymnastics), as well as 10 semi-structured parent interviews (Mage = 36.4; six female, four male), who each had a child between 3-5 years of age enrolled in one of the aforementioned programs. Results offer novel insight into toddler and preschooler children's sport, organized physical activity, active-play, and unstructured sport habits, while also highlighting common features and engagement patterns within early-years sport programs (e.g., structure, movement/sport skill focus, play-based activities). Findings suggest existing models (i.e., DMSP, CA´tA© & Fraser-Thomas, 2016; LTAD, CS4L, 2016) do not reflect or align with the delivery of or experiences within early-years sport programming. Future research is needed to determine what engagement patterns/program activities are optimal for toddler and preschooler development, to in turn contribute to refined versions of models, while acknowledging that sport participation is occurring prior to six years of age.Acknowledgments: Social Sciences and Humanities Research Council of Canada [grant no. 435-2016-1630], and Ontario Graduate Scholarship.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".