An Observational Study on Play and Physical Activity Associated with a Recreational Facility-Led Park-Based “Loose Parts” Play Intervention during the COVID-19 Pandemic
Bibliographic record
Abstract
Play is a human right, yet opportunities for unstructured play are declining. The COVID-19 pandemic further reduced children's play opportunities. We conducted an observational study of a novel community-based intervention (play hubs) that facilitated unstructured play by offering loose parts in parks (Calgary, Canada) during the pandemic. Our descriptive study included systematic observation using the System for Observing Children's Activity and Relationships During Play (SOCARP) and Tool for Observing Play Outdoors (TOPO) to capture physical activity, play, and social and environment interactions among children participating in the play hubs for 10-weeks in 2021 (n = 160) and 2022 (n = 147). Play hub attendance was low. Most children observed were aged 5 to 12 years (2021: 93% and 2022 98%), with boys and girls represented (2021: 58% male/42% female and 2022: 52% male/48% female). Standing, sitting, and moderate activity were common activities. Physical, exploratory, and expressive play were common, while digital, bio, and rule-based play were less common. Children typically played alone or in small groups and engaged with loose parts or played in the open spaces. The play hubs encouraged unstructured play and promoted positive social interactions among children, despite the challenges of implementing a community-based intervention under pandemic public health restrictions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".