Parents’ Perceived Barriers to Accessing Sports and Recreation Facilities in Ontario, Canada: Exploring the Relationships between Income, Neighbourhood Deprivation, and Community
Bibliographic record
Abstract
Sports and recreation facilities provide places where children can be physically active. Previous research has shown that availability is often worse in lower-socioeconomic status (SES) areas, yet others have found inverse relationships, no relationships, or mixed findings. Since children's health behaviours are influenced by their parents, it is important to understand parents' perceived barriers to accessing sports and recreation facilities. Data from computer assisted telephone interviews with parents living in Ontario, Canada were merged via postal codes with neighbourhood deprivation data. Multivariable logistic regression modeling was used to estimate the likelihood that parents reported barriers to accessing local sports and recreation facilities. Parents with lower household incomes were more likely to report barriers to access. For each unit increase in deprivation score (i.e., more deprived), the likelihood of reporting a barrier increased 16% (95% CI: 1.04, 1.28). For parents, the relationships between household income, neighbourhood-level deprivation, and barriers are complex. Understanding these relationships is important for research, policy and planning, as parental barriers to opportunities for physical activity have implications for child health behaviours, and ultimately childhood overweight and obesity.
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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) | 0.000 | 0.001 |
| 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".