Specialist or Nonspecialist Physical Education Teachers in Ontario Elementary Schools: Examining Differences in Opportunities for Physical Activity
Bibliographic record
Abstract
Research supports the position that specialists are the preferred providers of physical education in elementary (primary) school settings. We examined whether specialists delivered more physical education lessons and provided greater opportunities for moderate and vigorous physical activity and whether barriers to curricular and extracurricular physical activity opportunities in Ontario elementary schools differed for specialist and nonspecialist teachers. Using a cross-sectional design, a questionnaire was mailed to key informants in 599 randomly selected elementary schools in Ontario, yielding an 85% response rate. Most physical education in Ontario elementary schools is delivered by nonspecialists (63%). No self-reported differences were found between specialists and generalist teachers in the number of physical education lessons delivered per week, the minutes per physical education class, or in the amount of reported moderate or vigorous physical activity in lessons. Compared with specialists, respondents in schools in which generalist teachers taught PE perceived lack of training as a greater barrier to delivering physical education. Given the importance of subject knowledge in teaching effectively, there is still need to advocate for specialist physical education teaching in elementary schools. However, concerns about teaching specialism may be secondary to broader and more complex factors surrounding the delivery of physical education in elementary schools.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".