Symposium presentation #2: Supporting teachers' ability to foster children's physical literacy
Bibliographic record
Abstract
Children spend the majority of their waking hours within the school setting. As such, teachers have the potential to be key influencers on children's development. As part of a national surveillance study exploring children's physical literacy using the Canadian Assessment of Physical Literacy (CAPL), we employed a cross-sectional design to explore the effect of teacher training on the likelihood of children meeting recommended levels of physical literacy in a sample of 4,189 children aged 8 to 12 years (M = 10.72 years, SD = 1.19). Logistic regressions supported that children taught by teachers with physical education (PE) training were more likely to reach recommended levels of motor skill proficiency (OR = 0.77, 95% CI, 0.67-0.90) and have higher motivation and confidence levels (OR = 0.83, 95% CI, 0.72-0.95) than those whose teachers did not have PE training. These findings helped inform a knowledge translation event, the Healthy Bodies Healthy Minds Physical Literacy Summit. Immediate post-summit evaluation data from 16 teachers indicated they had strong intentions to develop, implement, and evaluate physical literacy initiatives in their classrooms (M = 4.82/5, SD = .41). Teachers exhibited significant increases in perceived physical literacy knowledge, t(9) = 2.44, p < .05; and skills to implement activities fostering physical literacy pre-post summit, t(10) = 4.66, p < .05. Using the CAPL data to provide context, this presentation will discuss challenges and potential for initiatives to enhance teachers' ability to deliver high quality physical activity experiences fostering children's physical literacy.Acknowledgments: RBC Learn to Play
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".