A cross-sectional study to examine constraints to sport participation among ethnically diverse female adolescents from Ontario, Canada
Bibliographic record
Abstract
Sport participation during adolescence is associated with good physical and mental health as well as social connectedness and greater well-being. Importantly, adolescence is a key developmental period when lifelong habit and behavioral patterns are shaped and when the benefits of sport are particularly beneficial to physical and psychological development. However, in Canada and internationally, adolescent females are consistency less active than males during adolescent years, are typically underrepresented in sports, and tend to drop out at disproportionate rates compared with their male peers. This cross-sectional study (2017-2019) aimed to examine associations between sport participation and individual, environmental, and task constraints for 825 ethnically diverse adolescent girls aged 13-19 years. Guided by Newell’s model on sport participation, analysis included a series of unadjusted and adjusted binary logistic regression models in order to examine individual, environmental, and task constraints as predictors of sport participation, as well as potential interactions between significant constraints and their association with sport participation. In the adjusted multivariate analyses, significant constraints to sport participation included weather (environmental), development and age (individual), and physical intensity (task), with no significant interactions. Overall, findings suggest that various constraints, particularly at the individual level (developmental) affect sport participation among diverse female adolescents. Future research should integrate mixed-methods to ensure a comprehensive examination of potential interactions of constraints. This can enhance understanding of complex and interacting factors, which can be integrated to lead to effective interventions, programs and policies that support adolescent female sport participation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".