Development and implementation of a municipal outdoor play policy for children and youth in Nova Scotia, Canada: a community case study
Bibliographic record
Abstract
Children and youth benefit from outdoor play; however, environments and policies to support outdoor play are often limited. The purpose of this paper is to describe a case study of the development of a municipal outdoor play policy in Nova Scotia, Canada. The outdoor play policy was developed by the Town of Truro with support from the UpLift Partnership, a School-Community-University Partnership in Nova Scotia, Canada. UpLift supports the health and well-being of school-aged children and youth using a Health Promoting Schools approach which identifies the important role of municipal government in creating healthy school communities. The UpLift Partnership and the municipality hosted online workshops for municipal staff, community leaders and partners that included content about the importance of outdoor play, barriers and facilitators to outdoor play, best practices for youth engagement, the policy development process, and how policy actions can support outdoor play. Workshop participants developed policy actions for their community of Truro, Nova Scotia to increase opportunities for outdoor play for children and youth. Following the workshops, a small team from the municipality and UpLift drafted an outdoor play policy and submitted it to Truro town council for approval. The outdoor play policy was adopted in Fall 2021 and has since informed recreation and municipal planning decisions. By presenting a case study of the development of this outdoor play policy, we hope other communities may be inspired to develop and adopt their own outdoor play policies to benefit children and youth in their communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".