Exploring the effects of imagery on components of physical literacy among children
Bibliographic record
Abstract
Physical literacy comprises four dynamic, interconnected components: affective (motivation and confidence), physical (physical competence), cognitive (knowledge and understanding), and behavioral (engagement in a physically active lifestyle; Whitehead, 2013). These components serve as the foundation of lifelong engagement in sport and physical activity, and thus identifying strategies that can help children become physically literate is paramount. The overall goal of our study was to determine whether imagery, coupled with physical training, could serve as one potential strategy. Specifically, the primary purpose was to examine the effects of a 4-week imagery intervention on: (a) the affective and physical components of physical literacy, and (b) imagery use and ability. A total of 9 children (male = 6, female = 3; Mage = 9.11, SD = .60) completed the intervention. Children in the experimental group received 4 weeks of guided imagery (3x/week), in addition to participating in their weekly sport program (soccer). Children in the control group also participated in their weekly sport program (basketball), but did not receive any guided imagery sessions. Analyses were conducted within a frequentist (independent samples t-test, paired samples t-test) and Bayesian (Bayesian independent samples t-test, Bayesian paired samples t-test) framework. Results revealed no between-group differences at post-intervention. Within-group analyses indicated that actual competence (p = .008, BF10 = 10.97) and perceived competence (p = .008, BF10 = 17.11) increased significantly from pre- to post-intervention for children in the experimental group. Practical and theoretical implications for enhancing children's physical literacy are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".