Development, implementation, and scalability of the Family Engagement in Research Course: a novel online course for family partners and researchers in neurodevelopmental disability and child health
Bibliographic record
Abstract
BACKGROUND: Since 2011 when the Canadian Institutes of Health Research launched the Strategy for Patient Oriented Research, there has been a growing expectation to embed patient-oriented research (POR) in the health research community in Canada. To meet this expectation and build capacity for POR in the field of neurodevelopmental disability and child health, in 2017 researchers and family leaders at CanChild Centre for Childhood Disability Research, McMaster University partnered with Kids Brain Health Network and McMaster Continuing Education to develop and implement a 10-week online Family Engagement in Research (FER) Course. MAIN TEXT: From its inception, the FER Course has been delivered in partnership with family leaders and researchers. The FER Course is innovative in its co-learning and community building approach. The course is designed to bring family partners and researchers together to co-learn and connect, and to develop competency and confidence in both the theory and practice of family engagement in research. Coursework involves four live online group discussions, individual review of course materials, weekly group activities, and a final group project and presentation. Upon completion of the FER Course, graduates earn a McMaster University micro-credential. CONCLUSIONS: To meet a need in building capacity in POR, a novel course in the field of neurodevelopmental disability and child health has been co-created and delivered. Over six years (2018-2023), the FER Course has trained more than 430 researchers and family partners across 20 countries. A unique outcome of the FER Course is that graduates expressed the wish to stay connected and continue to collaborate well beyond the course in turn creating an international FER Community Network that continues to evolve based on need. The FER Course is creating a growing international community of researchers, trainees, self-advocates, and family partners who are championing the implementation of meaningful engagement in neurodevelopmental disability and child health research and beyond. The course is internationally recognized with an established record of building capacity in POR. Its uptake, sustainability, and scalability to date has illustrated that training programs like the FER Course are necessary for building capacity and leadership in 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".