Stranger danger or good Samaritan? A cross-sectional study examining correlates of tolerance of risk in outdoor play among Canadian parents
Bibliographic record
Abstract
Abstract Background Negative parental perceptions of risk may restrict children’s opportunities for outdoor play. Excessively minimizing children’s exposure to risks in their environment may have a range of developmental consequences. The purpose of this cross-sectional study was to assess correlates of parental tolerance of risk among a large sample of Canadian parents. Methods In this cross-sectional study, a sample of 2,291 parents of 7–12 year olds completed online questionnaires assessing a range of potential individual (e.g., gender), social (e.g., neighbourhood cohesion), and environmental (e.g., walkability) correlates of parental tolerance of risk. Logistic regressions were created to examine associations between these factors and odds of being in the most risk averse quartile. The logistic regression was built in hierarchal steps relying on the Akaike information criterion (AIC) and pseudo R 2 for model progression. Results The final model had a pseudo R 2 of 0.18. Five out of seventeen correlates were associated with risk aversion in parents. Concerns about stranger danger were associated with a higher odds of risk aversion (OR = 2.33, 95%CI[1.93, 2.82]). A higher number of children in the home was associated with lower odds of risk aversion in parents (OR = 0.80, 95%CI[0.69, 0.92], and parents of children born outside of Canada had higher odds of being risk adverse when compared to parents born in Canada (OR = 2.13, 95%CI[1.54, 2.94]). Finally, being very concerned with COVID-19 increased the odds of risk aversion (OR = 3.07, 95%CI[1.93, 5.04], while having a household income of > 100,000 lowered the odds of risk aversion (OR = 0.56, 95%CI[0.36, 0.87]). Conclusions Tailored interventions that reframe perceptions of risk for parents are needed. Such interventions could reframe concerns about stranger danger which persist despite occurrences of stranger abduction being extremely rare. Interventions could also be targeted to immigrant families and those with fewer children as they appear to be more averse to risk. A complementary focus on examining how cultural background influences risk perceptions is needed in future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".