What children’s perspectives on safe and dangerous outdoor play can tell us about their risk-seeking and injury experiences: ‘You don’t feel right doing the thing that got you hurt’
Bibliographic record
Abstract
BACKGROUND: Unintentional injuries are a leading cause of children's hospitalisations and death globally and are thus a pressing public health concern. Fortunately, they are largely preventable, and understanding children's perspectives on safe and dangerous outdoor play can help educators and researchers identify ways to mitigate the likelihood of their occurrence. Problematically, children's perspectives are rarely included in injury prevention scholarship. In this study, we acknowledge children's right to have their voices heard by exploring the perspectives on safe and dangerous play and injury of 13 children in Metro Vancouver, Canada. METHODS: We employed tenets of risk and sociocultural theory and a child-centred community-based participatory research approach to injury prevention. We conducted unstructured interviews with children aged 9-13 years old. RESULTS: Through our thematic analysis, we identified two themes: (1) 'little' and 'big' injuries and (2) risk and danger. CONCLUSION: Our results suggest children differentiate between 'little' and 'big' injuries by reflecting on the potential loss of opportunities for play with friends. Further, they suggest children avoid play they perceive as dangerous, but enjoy 'risk-seeking' because it is thrilling and provides them with opportunities to push their physical and mental capabilities. Child educators and injury prevention researchers can use our findings to inform their communications with children and make play spaces more accessible to, fun and safe for children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".