Return to ringette from the COVID-19 pandemic: An updated RE-AIM evaluation of Ringette Canada’s small-area games guidelines
Bibliographic record
Abstract
During the 2019-2020 season, Ringette Canada introduced guidelines recommending that players under 10 years of age play small-area games with the aim of improving long-term development. That season, we partnered with Ringette Canada to evaluate provincial and local ringette administrators’ perceptions of the guidelines. Administrators reported mixed beliefs about the developmental benefits of small-area games, with only one provincial association and 16% of local associations planning to fully implement the guidelines. In March 2020, the COVID-19 pandemic forced a premature end to ringette programming and shifted priorities for Ringette Canada. To this end, we adapted our evaluation to assess (a) reasons for participating in ringette (or not) before and during the pandemic, (b) intentions to return to ringette, and (c) perceived benefits and drawbacks of small-area games. We surveyed ringette players aged 13-18 (n=294) and parents of ringette players aged 6-12 (n=47). Overall, 91% of participants reported that they/their child would play ringette in 2022-2023. Top-reported reasons for playing included accessible facilities/programs (49%), emphasis on participation not performance (47%), and social connections/inclusive spaces (36%). Those who did not intend to play cited lack of access to facilities/programs (67%), cost (60%), and safety concerns (53%). Perceptions of small-area games mirrored our 2019-2020 evaluation with participants reporting mixed beliefs about the benefits (e.g., enhanced skill development) and drawbacks (e.g., difficulty transitioning to full-ice). We will discuss how research partnerships can inform real-time decision-making for sport partners and outline next steps for Ringette Canada’s Children’s Ringette program.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".