Active play in childhood and the Long Term Athlete Development model: A qualitative examination
Bibliographic record
Abstract
This research study examined active play in childhood within the framework of the Long Term Athlete Development (LTAD) Model. Active play is defined as an unstructured physical activity that takes place in a child’s free time (Veitch et al., 2008). Seven varsity athletes (three males and four females) aged 18-25 participated in a 120 minute semi-structured retrospective focus group. Varsity athletes were interviewed on their active play experiences from three stages of their childhood. These three stages were taken from the LTAD model and included Active Start (0-6 yrs.), FUNdamentals (girls, 6-8 yrs.; boys, 6-9 yrs.), and Learn to Train (girls, 8-11 yrs.; boys, 9-12 yrs.). The focus group was transcribed verbatim and organized into meaning units using a deductive/inductive approach. Deductive analysis involves coding data into a pre-existing framework, whereas inductive analysis involves discovering themes and patterns that emerge out of participants’ responses (Patton, 2002). Coding was completed using QSR NVivo 10, with themes changing throughout the three age stages of the LTAD model. Overall there were eight main themes that emerged including: the environment (where participants completed active play), playmates, fundamental movement skills, barriers to active play, other skills developed through active play, reasons for active play, benefits of active play, and competence in active play. This study is the first to show how childhood active play contributes to the learning and execution of fundamental movement skills. Acknowledgments: This research was supported by a SSHRC grant awarded to the first author.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".