280 Knowledge brokers: community partners in youth injury prevention research
Bibliographic record
Abstract
Background (issue/problem) Building strong partnerships between researchers and the community in youth sport and recreational injury prevention to promote active living and prevent chronic disease is a timely priority. Engaging community partners throughout the research process, from planning to dissemination, is critical to ensure project success, effective knowledge translation (KT) and impact. Description of the problem Involvement of community stakeholders in research often includes pre-grant solicitation of support or end-phase KT activities. To facilitate optimal and timely input from key stakeholders at all stages of research, the Alberta Program in Youth Sport and Recreational Injury Prevention developed a Knowledge Broker (KB) model. Results (effects/changes) In alignment with research priorities, KBs were identified in relevant community partner organisations (Ever Active Schools, Hockey Calgary, WinSport). KBs bridge the gap between research, education and KT priorities within the academic institution and the community. KB activities include contributing to research questions, intervention development, subject recruitment, implementation and dissemination of findings. Linking researchers and knowledge users facilitates collaboration, a greater understanding of common and diverse goals, and new partnerships. The ideal outcome of these partnerships includes optimal knowledge exchange to maximise the uptake of research evidence for the greatest public health impact. A financial contribution from the research program helps support the commitment of KBs. Conclusions The integrated nature of the research program with academic and community stakeholder partnerships, creates an ideal setting for enhancing the impact of KT. KB involvement contributes significantly to achieving research objectives. KB contributions optimise the translation of research findings into injury prevention practice, programs and policies.
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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".