2025 position statement on active outdoor play: process and methodology
Bibliographic record
Abstract
Abstract Background The 2015 Position Statement on Active Outdoor Play highlighted the benefits of active outdoor play for child health and development and had significant impact on public policy, law, philanthropy, and research in Canada and internationally. To celebrate its 10th anniversary, we led an update to the Position Statement that is global in scope and inclusive of all human age groups. The goal of this manuscript is to outline the process and methodology involved in developing the 2025 Position Statement on Active Outdoor Play. Methods The process involved several key steps: (1) establishing an international leadership group; (2) forming an international steering committee; (3) conducting an environmental scan of historical key events in active outdoor play; (4) using text-mining and linked network analyses to identify core research themes and map relationships among outdoor play organizations; (5) developing a conceptual framework through a consensus-building process; and, (6) using this conceptual framework to guide the completion of a series of reviews used to inform the 2025 Position Statement. Results The environmental scan identified 136 key events that provided historical context for the evolution of active outdoor play. Text-mining and linked network analyses revealed 10 core research themes and mapped relationships among key organizations and individuals in the field. Informed by these themes and concepts, a draft conceptual framework was developed and circulated to the > 130-person steering committee for review and consensus. The final version of the conceptual framework encompassed 9 key themes, which guided the focus of 18 reviews used to inform the scope and content of the 2025 Position Statement on Active Outdoor Play. Conclusions A three-year-long process involving an international leadership group and >130 person steering committee led to the development of an environmental scan, text-mining and linked network analysis, and a conceptual framework including 9 key themes with bidirectional relationships with active outdoor play that guided the development of 18 reviews. Collectively, this work served to inform the updated Position Statement on Active Outdoor Play, which aims to encourage humans of all ages to play, learn, teach, grow, and thrive outdoors and serve as caring stewards of the land, water, air, plants, animals, and each other.
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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 | 0.249 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".