Peer support for families of children with complex needs: Development and dissemination of a best practice toolkit
Bibliographic record
Abstract
BACKGROUND: Benefits of peer support interventions for families of children with disabilities and complex medical needs have been described in the literature. An opportunity to create an evidence-informed resource to synthesize best practices in peer support for program providers was identified. The objective of this paper is to describe the key activities used to develop and disseminate the Peer Support Best Practice Toolkit. METHODS: This project was led by a team of knowledge translation experts at a large pediatric rehabilitation hospital using a knowledge exchange framework. An integrated knowledge translation approach was used to engage stakeholders in the development process through focus groups and a working group. To capture best practices in peer support, a rapid evidence review and review of related resources were completed. Case studies were also included to showcase practice-based evidence. RESULTS: The toolkit is freely available online for download and is structured into four sections: (a) background and models of peer support, (b) case studies of programs, (c) resources, and (d) rapid evidence review. A communications plan was developed to disseminate the resource and generate awareness through presentations, social media, and champion engagement. Eight months postlaunch, the peer support website received more than 2,400 webpage hits. Early indicators suggest high relevance of this resource among stakeholders. CONCLUSIONS: The toolkit format was valuable to synthesize and share best practices in peer support. Strengths of the work include the integrated approach used to develop the toolkit and the inclusion of both the published research literature and experiential evidence.
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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 | 0.081 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".