‘Active & Safe Central’: development of an online resource for the prevention of injury in sport and recreational activity
Bibliographic record
Abstract
BACKGROUND: Sport and recreation related injuries exert a significant cost on the healthcare system. As prevention researchers and practitioners, we have a responsibility to provide guidance towards prevention to those who participate in sport and recreation, and those that coach, treat and parent children that participate. The objective of this project was to use an integrated knowledge translation approach to develop an end user-driven digital platform that provides injury prevention information and resources across 51 sport and recreational activities. DESIGN: We used an integrated knowledge translation approach to scope and develop an online sport and recreational injury prevention resource. A project team was formed that included end users-coaches, parents and athletes, injury researchers and practitioners, as well as members of a digital design team. All members of the project team informed the development process, including a review of literature and existing resources, the translation of evidence and development of the platform. At all stages of development, members of the project team cocreated knowledge for the tool, including forming the research questions, the approach, feasibility and development of outcomes. CONCLUSION: The 'Active & Safe Central' (https://activesafe.ca/) platform provides web-based sport injury and prevention information. This user-friendly, web and mobile accessible platform can increase the reach, awareness and implementation of prevention programming in sport and recreational activity.
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 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".