Editorial: Co-design of rehabilitation programming
Bibliographic record
Abstract
This Research Topic consists of 10 articles, each describing important aspects of co-design approaches and how they can be applied in a rehabilitation setting. Among the articles, a common theme was using co-design as a method of identifying factors that may influence the success of various rehabilitation programs, educational tools, or technologies. Studies included multiple and diverse stakeholder groups in their research, demonstrating the importance of using a comprehensive approach to gather perspectives.A common theme among the original research and methods papers was the use of co-design in working towards discrete outcomes rather than impacting service delivery or governance. Reitzel et al. (2023) used a co-design approach that included caregivers, clinicians, and healthcare managers and discussed innovative solutions to enhance access and engagement in pediatric telerehabilitation. Through these discussions, they found that communication, consistency and connection were key factors that could enhance engagement in pediatric 2024) shares fundamental principles that are essential to implementing co-design approaches in a meaningful and authentic way. This article leaves readers with the challenge of progressing co-designed research towards co-production, a collaborative approach that centres equitable and ethical practices focused on reflective dialogue (Bourke et al. ( 2024)).The articles submitted to this topic were focused on the fields of pediatric and spinal cord injury rehabilitation. These populations often require extensive and ongoing rehabilitation care throughout much of their life, emphasizing the need for care to be person-centered. A systematic review found that co-design approaches are most often described in the fields of mental health, primary care, and pediatrics, and that each field is distinctive and will benefit from different implementation strategies (2). Lived experience engagement and integrated knowledge translation are also at the forefront in spinal cord injury research and practice, resulting in the recent formation of groups that are focused on bringing together people with lived experience, clinicians, and researchers to bring about change (4). Knowing that co-design approaches are impactful in any population, we encourage rehabilitation researchers and decision-makers to consider how they can best use co-design to support each unique population they support.The articles in this research topic highlight the importance of co-design methods in developing and evaluating rehabilitation programs, educational tools, and technologies. Using a co-design approach ensures rehabilitation practices are person-centered and are meeting the needs of key stakeholders, including patients, caregivers, and clinicians, as well addressing enablers and barriers within the healthcare system. It is imperative that co-design approaches are carried out intentionally and with people with lived experience at the core, in order for the field to move forwards toward co-production. As the articles in this Research Topic primarily focused on initial development of rehabilitation programs, educational tools, and technologies, it will be important for future research to use co-design to evaluate progress and person-centred outcomes, as well as to reflect on larger impacts in service delivery models and organizational structures, which typically require a higher level of engagement.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".