Integrating outdoor education into physical education and health teaching: Three case studies
Bibliographic record
Abstract
Introduction. Young people in Quebec do not do enough outdoor activity, despite the health benefits it brings. To address this issue, the Quebec Education Program recommends the practice of outdoor education, especially as part of the physical education (PE) and health curriculum. Despite this, PE teachers seldom integrate outdoor education into their yearly teaching programs. This study aims to identify the factors that promote or limit the use of outdoor education in PE, and also to identify what PE teachers need in order to improve its use, through a better understanding of current school practices. Methods. Three case studies were conducted with PE teachers selected with inclusion criteria. They participated in a semi-structured interview that was audio recorded and transcribed. Its content was then analyzed. Results. Implementing outdoor education in Quebec presents many challenges, but solutions do exist. PE teachers do not appear to have any measures by which they can identify learning progression linked to the curriculum. They also cite the lack of educational and support tools as being an issue. The links between outdoor education, health education, and environmental education should be specified and developed. Conclusion: This work has clarified the use of outdoor education in terms of its influences, and has specified the factors that help or hinder its implementation in schools. It has also offered avenues for reflection when it comes to increasing its use in the school environment, including looking at accessibility and cost.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".