Support Factors and Barriers for Outdoor Learning in Elementary Schools: A Systemic Perspective
Bibliographic record
Abstract
Background Outdoor learning offers clear physical, cognitive, social-emotional and academic benefits for children and yet, it is considered a grassroots approach to teaching and learning in elementary schools.Purpose We examined teachers’ perspectives on barriers and supports for outdoor learning in public elementary schools.Methods Thirty-six teachers in (urban and rural) British Columbia, Alberta, and Ontario (all female; Mean age = 43.84, SD = 10) participated in one of five virtually administered, semi-structured focus groups. Questions/prompts facilitated a discussion on teachers’ experiences with barriers and supports for outdoor learning. Thematic analysis was used to identify main themes.Results Four interrelated themes and further sub-themes were found: 1) Teacher characteristics: interest/motivation to teach outdoors, preparedness, confidence in handling risks; 2) Systemic factors: principal support, school/district policies, funding/resources, curriculum, school schedule; 3) Culture: school culture, societal beliefs about education, family backgrounds; 4) Environmental factors: weather, built/natural environment, hazards.Discussion Systemic support is needed to integrate outdoor learning in schools.Translation to Health Education Practice The findings in this study are relevant to health education specialists particularly focused on elementary school education.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".