Translating Emergency Knowledge for Kids (TREKK)
Bibliographic record
Abstract
Background / Introduction In Canada, many sick and injured children receive care in emergency departments (EDs) outside of specialized children’s hospitals, where access to pediatric-specific resources and training can be limited. This knowledge gap led to the creation of Translating Emergency Knowledge for Kids (TREKK) in 2011, a national non-profit initiative dedicated to improving emergency care for children. TREKK co-develops and mobilizes evidence-based, practical resources, such as bottom-line recommendations and treatment algorithms, to healthcare providers (HCPs) across Canada, ensuring children receive optimal care regardless of where they are treated. Methods / Implementation TREKK’s resource development process begins by identifying gaps in pediatric emergency care through consultations with emergency healthcare providers, researchers, national organizations, and the public. By integrating diverse perspectives, TREKK ensures that the resources produced are comprehensive, relevant, and based on the latest evidence. The synthesized evidence is then transformed into practical, concise tools with input from pediatric researchers, clinicians, and parents. These resources undergo a national review and approval process before being disseminated to healthcare providers. To maintain relevance, they are updated every two years. Results / Evaluation To date, TREKK has co-created 105 resources covering 40 pediatric emergency topics, with over 165,000 downloads from trekk.ca. The co-development process has demonstrated national reach and engagement. The 2025 updates focus on two high-priority areas: Fever and Cannabis. These topics are of particular importance for emergency nurses because they are frequently encountered in pediatric emergency care and require timely, evidence-based management. Implications / Lessons Learned This work highlights the importance of national collaboration in closing knowledge-to-practice gaps in pediatric emergency care. By co-developing and updating resources with frontline providers, researchers, and families, TREKK ensures that evidence-based tools remain practical, accessible, and relevant. The updated Fever and Cannabis resources will better support emergency nurses in delivering safe, timely, and effective care for children. Future work will continue expanding resources and sustaining their national uptake.
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 | 0.038 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".