Strengthening Canadian Child and Youth Advocacy Centres through coordinated research and knowledge sharing: Establishing a Canadian Research and Knowledge Centre
Bibliographic record
Abstract
It is crucial to create a platform for coordinating, building, and sharing knowledge to guide practice and policy development among both established and emerging Child and Youth Advocacy Centres (CYACs). CYACs bring together multidisciplinary professionals from various systems to collectively address child abuse and support the healing of children, youth, and their families from trauma and its impacts. We collaborated with partners from academic, practice, and policy sectors through a co-design process to establish a Canadian Child and Youth Advocacy Research and Knowledge Centre. This discussion paper will start by highlighting the importance of community-academic partnerships. We will then outline the processes used to develop and establish the Research and Knowledge Centre. Finally, we will describe the outcomes of establishing the Research and Knowledge Centre, including the guiding principles, priority action areas and the research agenda, along with considerations for ongoing work and collaboration in this field. The goal of this Research and Knowledge Centre is to equip CYAC leaders, practitioners, and policymakers with contextual and rigorous evidence to inform decisions that will improve support for children, youth, and families impacted by child abuse. • The merits of the Child and Youth Advocacy Centre (CYAC() approach are clearly recognized by practitioners, children, and families, the evidence-base around process, effectiveness, and impact is emerging. • Given the proliferation of CYACs in Canada, there is an important opportunity and need to embed rigorous and contextually sensitive research approaches into the Canadian CYAC infrastructure to ensure evidence-based approaches in the CYAC setting and ultimately, contribute to ongoing improved outcomes for young people impacted by child abuse. • The vision of the Research and Knowledge Centre is to generate and integrate new evidence about child abuse into everyday CYAC practice in Canada. As part of this vision, the Research and Knowledge Centre has several key principles that drive its work, including addressing the need for context-specific and practice-relevant research that is developed collaboratively with academics and community partners to ensure appropriate implementation of knowledge to practice and policy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".