Engaging families, enhancing research: optimizing rehabilitation research through co-creation of a Family Engagement in Research Framework
Bibliographic record
Abstract
PURPOSE: To describe the systematic revision of a Family Engagement in Research (FER) Framework within a pediatric rehabilitation context. METHOD: Revision of the Framework involved: 1. Facilitating co-creation workshops with clients, families, staff, trainees and researchers; 2. Updating the Framework and developing supplementary tools to facilitate Framework use; and 3. Disseminating the updated Framework and accompanying tools. RESULTS: The revision process resulted in an updated FER Framework at Holland Bloorview Kids Rehabilitation Hospital (HBKRH), which highlights three partnership roles families can play throughout the research process: Family Advisor, Family Partner and/or Lived Experience Educator. Additional products were created, including a guiding principles document, as well as a user guide to facilitate Framework use in practice. Due to the COVID-19 pandemic, a virtual launch was conducted to disseminate the Framework and accompanying tools. CONCLUSION: The updated Framework and accompanying tools reflect the current state of the evidence on family engagement in research, in addition to the needs of the families and organizational context at HBKRH. The hope is that other organizations can learn from the steps taken to optimize family engagement in research.
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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 | 0.500 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".