How an early learning and child care program embraced outdoor play: A case study
Bibliographic record
Abstract
Research indicates outdoor play influences children’s physical, cognitive and social-emotional well-being, but there are barriers to implementation in early learning settings. This study explores an early learning and child care (ELCC) program achieving success with outdoor play to identify strategies that may help overcome barriers and support outdoor play in similar contexts. Focus groups and interviews were conducted with ELCC program Early Childhood Educators (ECEs) and facilitators, school teachers and principal, and government staff. Data also included relevant documentation and photographs of the outdoor play spaces. Thematic analysis of all data was completed, resulting in a description of the ELCC program’s outdoor play space and practices and factors that may be influencing these identified practices. Six themes or influencing factors were identified: 1) outdoor play, including loose parts and risky play, is valued; 2) outdoor play is promoted and engaged in by others; 3) space and resources are available; 4) communication and engagement happens; 5) leaders are integral; and 6) partnerships and collaboration are essential. Using Bronfenbrenner’s ecological systems model, this research identifies outdoor play implementation strategies that may provide guidance to ELCC stakeholders such as ECEs and policymakers. To overcome outdoor play challenges, considerations should be made to purposefully target and engage multiple subsystems and stakeholders as described in this study for greatest impact.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".