Examining the Impact of a Teaching Games for Understanding Approach on the Development of Physical Literacy Using the Passport for Life Assessment Tool
Bibliographic record
Abstract
Purpose: The purpose of this study was to investigate the impact of an 8-week after-school intramural program that adopted a Teaching Games for Understanding (TGfU) approach to facilitate the development of elementary-school-aged children physical literacy. Methods: Using Physical and Health Education Canada’s Passport for Life tool, 22 participants took part in a battery of assessments consistent with characteristics of physical literacy. These measures were (a) active participation, (b) living skills, (c) fitness skills, and (d) movement skills. Each category of assessment included three submeasures for a total of 12 indicators of physical literacy. Participants were assessed at the beginning of the PlaySport Intramural Program and then 8 weeks later following participation in a series of after-school TGfU lessons designed using the PlaySport program. Results: Of the 12 indicators of physical literacy, the majority of participants reported higher scores at the end program for 10 of the indicators. Significant (p < .004) improvements were seen in balance and stability skills, cardiovascular endurance, participation in diverse environments, and interest in participating in diverse activities. No improvements were seen in kicking skills and interacting with others. Discussion/Conclusion: These results provide support for the hypothesis that the use of pedagogical approaches such as TGfU can be effective at facilitating certain components of children’s development of physical literacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".