Creating a rehabilitation living lab to optimize participation and inclusion for persons with physical disabilities
Bibliographic record
Abstract
We present an on-going multidisciplinary and multisectorial strategic development project put forth by the Centre for Interdisciplinary Research in Rehabilitation of greater Montréal (CRIR) in Quebec, Canada and its members, in collaboration with a Montréal “renovation-ready” shopping mall, local community organizations, and local, national and international research and industrial partners. Beginning in 2011, within the context of the Mall as Living Lab (MALL), more than 45 projects were initiated to: (1) identify the environmental, physical and social obstacles and facilitators to participation; (2) develop technology and interventions to optimize physical and cognitive function participation and inclusion; (3) implement and evaluate the impact of technology and interventions in vivo. Two years later and working within a participatory action research (PAR) approach, and the overarching WHO framework of the International Classification of Functioning, Disability and Health (ICF), we discuss challenges and future endeavors. Challenges include creating and maintaining partnerships, ensuring a PAR approach to engage multiple stakeholders (e.g. people with disabilities, rehabilitation and design researchers, health professionals, community members and shopping mall stakeholders) and assessing the overall impact of the living lab. Future endeavors, including the linking between research results and recommendations for renovations to the mall, are also presented.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".