Active & Safe Central: using a mixed-methods design and the RE-AIM framework to evaluate a sport and recreational injury prevention resource for practitioners in Canada
Bibliographic record
Abstract
OBJECTIVES: An online, evidence-based resource was created to support the development of sport and recreational injury prevention programmes. The resource, called Active & Safe Central (www.activesafe.ca), provides evidence-based information across the public health approach for a number of sport and recreational activities. The objective of this project was to evaluate the perceived usefulness of Active & Safe Central as an educational tool. DESIGN: A mixed-methods study design, guided by the RE-AIM (reach, effectiveness, adoption, implementation and maintenance) framework, was used to evaluate Active & Safe Central. SETTING: Data were collected using an online survey, available to all users of the site, and focus groups (n=2) that took place in Vancouver and Toronto, Canada. PARTICIPANTS: Survey respondents (n=87) were recruited online, including parents, coaches, youth and adult athletes, and teachers. Focus group participants (n=16) were key stakeholders and end users, recruited from academia, local health sectors, sport and recreational organisations, and not-for-profit injury prevention organisations. RESULTS: Post launch, there were 1712 users visiting the website 2306 times (sessions), with the majority representing new users, over returning users (87.5% and 12.5%, respectively). There were 6340 page visits, with the most popular pages including soccer, playgrounds and ice hockey. Active & Safe Central was reported as a credible source of evidence-based sport and recreational information and that the site would be recommended to others. Information collected from focus group participants was used to inform necessary adaptations to the online platform, including critical navigation issues, visualisations and interactivity. The major themes that emerged from focus group and survey data included increased awareness of injury prevention recommendations and that the recommendations would be used in one's own sporting activity and/or practice. CONCLUSIONS: The results of the evaluation suggest the tool is a useful resource for sport and recreational injury information that has significant potential to impact prevention practice.
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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 | 0.071 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".