MétaCan
Menu
Back to cohort
Record W4406113156 · doi:10.3389/fresc.2024.1537063

Editorial: Co-design of rehabilitation programming

2025· editorial· en· W4406113156 on OpenAlexaff
Janelle Unger, Dalton L. Wolfe, John Bourke, James Middleton

Bibliographic record

VenueFrontiers in Rehabilitation Sciences · 2025
Typeeditorial
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsParkwood InstituteWestern University
Fundersnot available
KeywordsRehabilitationStakeholderHealth careTheme (computing)Service delivery frameworkPsychologyService (business)Medical educationMedicineComputer scienceKnowledge managementEngineering ethicsNursingPublic relationsEngineeringBusinessPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.023
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.073
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.002
Science and technology studies0.0040.005
Scholarly communication0.0100.006
Open science0.0040.003
Research integrity0.0160.014
Insufficient payload (model declined to judge)0.0230.008

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.

Opus teacher head0.078
GPT teacher head0.448
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Rehabilitation SciencesSame topicMental Health and Patient InvolvementFrench-language works237,207