Elementary school generalist teachers’ perceived competence to deliver Ontario’s daily physical activity program
Bibliographic record
Abstract
The objective of this study was to examine the perceived competency of elementary school generalist teachers to deliver the government-mandated Daily Physical Activity (DPA) program. An e-survey was circulated to 20 elementary schools in a southern Ontario school board district. One hundred and thirty-six generalist teachers completed the survey from an initial pool of approximately 261 (response rate = 69%). Multiple linear regression was used to establish model fit and the prediction of participants’ perceived competence (dependent variable) to deliver DPA based on their motivation, school environment, and skills (independent variables). The most significant finding of this research is that nearly half of teachers surveyed (46%) reported that this mandatory program is not active in their schools. Additional analysis suggests that a majority of elementary teachers in this district lack specific DPA training and the motivation to teach the DPA program. The results not only suggest that the DPA program is not uniformly operationalized across the school board district, they also suggest that teacher competency may be a precondition for the DPA or other physical activity programs in elementary schools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".