USING AN INTEGRATED KNOWLEDGE TRANSLATION APPROACH TO INFORM THE DEVELOPMENT AND EVALUATION OF ACTIVE & SAFE CENTRAL: AN ON-LINE PLATFORM OF EVIDENCE-BASED SPORT AND RECREATIONAL INJURY INFORMATION
Bibliographic record
Abstract
Background: Sport and recreational injuries place a significant burden on the health care system and represent a large proportion of all injuries that report to emergency departments in Canada. An initiative to address this significant burden was to develop of an on-line platform providing information on the incidence of injury, risk factors, and effective interventions to reduce the risk of injury in over 51 sport and recreational activities. The information was collected by systematic review of the evidence which was critically appraised, synthesized and translated into an on-line platform, called Active and Safe Central. The objective of this project was to use an integrated knowledge translation approach to develop and evaluate the Active and Safe Central on-line tool. Methods: A mixed-methods design was used to develop and evaluate the Active and Safe Central Tool. Data was collected using a two-phased focus group format as well as an on-line survey, embedded in the on-line tool. Participants were recruited using purposive sampling and included a variety of stakeholders including parents, athletes, coaches, athletic therapists, injury prevention researchers and practitioners from local sport organizations. Focus groups were designed to be 60-minutes in duration and used a semi-structured format, led by a trained facilitator, to collect specific information on the design, use and information contained in the tool. The focus groups were both audio and visually recorded, with one project team member taking notes to supplement data collection. The on-line feedback form was created in consultation with experts in evaluation from the digital team as well as injury prevention researchers. The first focus group, at the build and mock-up phase collected information on: A) participants’ previous experiences with injury prevention resources and B) feedback on the proposed design, perceived usefulness and suggestions for the tool on use and content. The second focus group collected information on the interactivity of the tool, including live navigation with the beta version of the digital platform. Participants provided feedback on the functionality, visual appeal and usefulness of the site. Information from the focus groups and on-line feedback forms was synthesized and used to inform the design and reimaging of the Active and Safe Central tool during build, mock-up, beta and live versions. Results: Information collected from focus group participants was used to inform necessary changes in the on-line platform including critical navigation issues, visualizations, and interactivity. Initial post-launch results from the focus group (n=14) and from the on-line survey (n=87) suggest that the tool is a helpful resource for sport and recreational injury information (88.5% agreement). The majority of users agreed that the tool was easy to use (89.6%), that they learned something new after using the site (89.6%) and that they would use the recommendations presented in the prevention section of the site. Finally, 86.2% responded affirmatively to share the information gathered on the site, with others in their community. Conclusions: The Active and Safe Central resource was developed using an integrated knowledge translation approach with evaluation indices embedded throughout the process. Information collected through focus groups was critical in the initial development and subsequent iterations of the on-line tool. Initial evaluation results suggest the tool is an effective 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 | 0.130 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".