Hockey youth in Canada: Parents’ and coaches’ beliefs towards the half-ice game model for U9 hockey
Bibliographic record
Abstract
Despite the advantages of small area games in youth sport, some challenges remain regarding the implementation of the half-ice gameplay model in Canada. In youth sport, establishing a good line of communication between parents and coaches is a crucial step for a positive environment. The purpose of this study is to provide further knowledge on the mechanisms associated with parents' and coaches' perceptions regarding the half-ice model in Canada. Data came from a national survey distributed across Canada (N = 6 372). Parents and coaches completed questionnaires that assessed attitudes, norms and perceived facilitators-obstacles to half-ice hockey. Parents-coaches' preferences towards the playing format and sociodemographic variables were also measured. Structural equation modelling was performed to verify associations between each variable. Beliefs were a key factor in parents-coaches' preferences regarding the playing format. Previous sport background and knowledge about half-ice hockey were associated with favorable predispositions. Hockey associations administrators should consider parents and coaches' predispositions in program implementation and should design promotional campaigns adapted to their members' predispositions towards half-ice hockey. This research underlines the key factors to consider in successful program implementation in youth sport.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".