"little people, little area": A RE-AIM evaluation of ringette Canada's small-area games guidelines for children's ringette
Bibliographic record
Abstract
In 2019, Ringette Canada introduced the guidelines for its new grassroots program with the aim of improving long-term player development. These guidelines recommend that players under 10 years of age (U10) participate in small-area games (e.g., half-ice, cross-ice). We partnered with Ringette Canada to evaluate the reach, effectiveness, adoption, implementation, and maintenance of the guidelines using a three-phase, multi-method approach. In Phase 1, we interviewed one administrator from each provincial ringette association (n = 9). In Phase 2, local ringette administrators completed online surveys (n = 121). In Phase 3 (scheduled for the 2021-2022 season), video observation will be used to compare participants' developmental outcomes in small-area and full-ice game formats. Results from Phases 1 and 2 demonstrate that provincial ringette administrators were aware of the guidelines and believed in the benefits of small-area games. However, only one association planned to fully implement the guidelines up to the U10 level. In contrast, local ringette administrators reported mixed levels of awareness and beliefs about small-area games. Although 83% of local associations had adopted small-area games some or all of the time, implementation varied widely. Less than one-third planned to implement the guidelines at the U9 (30%) and U10 (16%) levels. Although plans to implement the guidelines at the U8 and U9 levels in future seasons appeared secure, plans for implementation for U10 players were broadly uncertain and pointed to a need for data to drive decision-making, promote buy-in from local ringette associations, and allot time to develop a strong implementation plan.Acknowledgments: This research was funded by a 2019-2020 SIRC Researcher-Practitioner Match Grant and a SSHRC Partnership Engage Grant (892-2019-3064).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".