A Descriptive Profile of Physical Education Teachers and Related Program Characteristics in Alberta
Bibliographic record
Abstract
A survey of teachers and principals in Alberta was conducted to gain a descriptive profile of who is leaching physical education (PE) and to assess the relationship between PE specialists and variables associated with program delivery. A probability-sampling procedure was used to obtain a representative sample of schools. In these schools nonprobability procedures were used to recruit teachers. A total of'480 teachers' and 162 principals' questionnaires were returned. Although 50% (n=236) of PE teachers in the sample were classified as PE specialists (i.e., had either a degree, major or minor, in PE or a closely related area), there was a significant gap in the number of PE classes being taught by division. Of the 1,219 PE classes surveyed in this study, PE specialists taught 49% and 55% of classes at the elementary levels (Divisions I & 11) compared with 91% of junior high (Division III) and 90% of secondary (Division TV) PE classes. Significant differences were found between PE specialists and non-PE specialists on a number of items including perceptions of preparedness, teaching enjoyment and competence to teach PE, the number of PE specialists across grade levels, and the percentage of time devoted to PE in the timetable. Implications with respect to implementing PE specialists across all grades and the need for future pedagogical research to investigate the effect of PE specialists are also 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".