Let's get physically literate: The teacher candidate's journey into physical education
Bibliographic record
Abstract
Childhood obesity and early onset of chronic disease are growing problems among Canadian youth (WHO, 2013). Recently, physical literacy (Whitehead, 1987, 1993) has gained attention as a vehicle through which to introduce the foundations of healthy physical activity patterns. Current conceptualizations of physical literacy relate to “competence and confidence in a wide variety of physical activities in multiple environments that benefit the healthy development of the whole person” (PHE Canada, 2013). Given many school-based physical education (PE) programs are failing to facilitate minimum physical activity engagement among children and youth (Stone et al., 2012), there is a push towards the promotion of physical literacy during compulsory education (Whitehead & Murdoch, 2006). While PE curricula are often pinpointed as the centre of poor programming (Tinning, 1990), little research has focused on understanding the learning that occurs within teacher training programs. This study explored new PE teachers’ perceptions of physical literacy and it’s implementation into current classroom practice. Data was collected from 10 new teacher through the use of a semi-structured interview guide. Preliminary findings suggest participants valued the concept of physical literacy, but had limited understanding as to how to implement or evaluate physical literacy in the classroom, or broader contexts. Further, participants experienced disconnect between the provision of resources, their training in physical and health education, and the ever-evolving health and physical education curricular landscape. Practical implications and directions for further research will be 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".