Examining multi-sport programming for preschoolers: A case study
Bibliographic record
Abstract
In 2017, 46% of preschool-aged children (3-4 years of age) participated in organized lessons, leagues, or team sports in Canada (ParticipACTION, 2018), reflecting an increase in structured sport programming for children in their early years. Alongside growing participation rates, continued attention has been given to the merits of early sport diversification (sampling), compared to single-sport participation (specialization) in child and youth sport research and practice (e.g., Cote et al., 2009; Jayanthi et al., 2013; Myer et al., 2016). The purpose of this exploratory case study was to gain understanding of a children's multi-sport (diversification promoting) program in Eastern Ontario geared specifically towards preschoolers. Data were collected through semi-structured interviews with four parents (3 mothers, 1 father; Mage = 35 years), four children aged 3-4 enrolled in a 4-month session, in addition to the program coach; six external researcher observations were also conducted. Thematic analysis (Braun & Clarke, 2006) revealed findings related to (a) the programming experience for preschoolers, (b) the program structure and philosophy as delivered by the coaches, and (c) the potential benefits of multi-sport programming for young children (related to accessibility, the development of sport competence, and life-skill acquisition). Results provide in-depth insight into the utility of multi-sport programming as viable early-years sport participation contexts, as well as address areas for improvement, particularly given the importance of quality early learning experiences in sport for long-term participation (Kirk, 2005). Study challenges and limitations related to engaging in research with young children are also discussed.Acknowledgments: This work was supported by Social Sciences and Humanities Research Council of Canada under Grant #435-2016-1630, as well as Ontario Graduate Scholars (OGS).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".