Play-Friendly Communities in Nova Scotia, Canada: A Content Analysis of Physical Activity and Active Transportation Strategies
Bibliographic record
Abstract
The Play-Friendly Cities framework describes key municipal actions and indicators which support a community’s playability and can positively influence children’s health behaviors and quality of life. The purpose of this study was to conduct a content analysis of Nova Scotia physical activity (PA) and active transportation (AT) strategies by applying the playability criteria in the Play-Friendly Cities framework. Methods: PA and AT strategies from communities across Nova Scotia were assessed using the Play-Friendly Cities framework. Strategy content was analyzed based on indicators across four themes: participation of children in decision making, safe and active routes around the community, safe and accessible informal play environments, and evidence-informed design of formal play spaces. Results: Forty-two (28 PA,14 AT) strategies were reviewed and all included statements reflective of at least one indicator (8 ± 4; range: 1–14). Content about safe and active routes around the community was most prevalent (41 plans, 812 mentions), while participation of children in decision making was least frequently presented (18 plans, 39 mentions). Content about safe and accessible informal play environments (31 plans, 119 mentions) and evidence-informed design of formal play spaces (28 plans, 199 mentions) was also present. Conclusions: All PA and AT strategies included some content reflective of a Play-Friendly City; however, there was great variability in the number of included indicators. This summary provides key information on opportunities, such as increasing meaningful involvement of children in decision making, that can inform future municipal actions and policies to improve a community’s playability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".