Co‐development of the ENVISAGE‐Families programme for parents of children with disabilities: Reflections on a parent–researcher partnership
Bibliographic record
Abstract
INTRODUCTION: In childhood disability research, the involvement of families is essential for optimal outcomes for all participants. ENVISAGE (ENabling VISions And Growing Expectations)-Families is a programme comprising five online workshops for parents of children with neurodevelopmental disorders. The workshops aim to introduce parents to strengths-based perspectives on health and development. The research is based on an integrated Knowledge Translation (iKT) approach, in which knowledge users are involved throughout the research process. This article is co-authored by the ENVISAGE health service researchers (N = 9) and parent partners (N = 3) to describe the process through which we co-developed and implemented the workshops. METHODS: Collaborative auto-ethnography methods, based on a combination of interviews, qualitative surveys, and discussions held to complete the Guidance for Reporting Involvement of Patients and Public-2 tool, were used to describe the co-design process, the benefits gained, and lessons learned. FINDINGS: Parents (n = 118) were involved in developing and implementing the ENVISAGE workshops across the different phases, as partners, collaborators, or participants. Three parents were involved as investigators throughout. We identify seven key ingredients that we believe are necessary for a successful parent-researcher working relationship: (i) consistent communication; (ii) clear roles and expectations; (iii) onboarding and feedback; (iv) flexibility; (v) understanding; (vi) self-reflection; and (vii) funding. CONCLUSION: Patient and family engagement in research is a rapidly growing area of scholarship with new knowledge and tools added every year. As our team embarks on new collaborative studies, we incorporate this knowledge as well as the practical experience we gain from working together.
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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 | 0.059 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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; 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".