Rehabilitation Living Lab in the Mall Community of Practice: Learning Together to Improve Rehabilitation, Participation and Social Inclusion for People Living with Disabilities
Bibliographic record
Abstract
Communities of practice (CoP) can facilitate collaboration between people who share a common interest, but do not usually work together. A CoP was initiated and developed including stakeholders from clinical, research, community and governmental backgrounds involved in a large multidisciplinary and multi-sectorial project: the Rehabilitation Living Lab in a Mall (RehabMaLL). This study aimed to evaluate the structure, process and outcomes of this CoP. A single case-study, using mixed-methods, evaluated the RehabMaLL CoP initiative after one year, based on Donabedian's conceptual evaluation model. Forty-three participants took part in the RehabMaLL CoP with 60.5% (n = 26) participating at least once on the online platform where 234 comments were posted. Four in-person meetings were held. Members expressed satisfaction regarding the opportunity to share knowledge with people from diverse backgrounds and the usefulness of the CoP for the RehabMaLL project. Collaboration led to concrete outcomes, such as a sensitization activity and a research project. Common challenges included lack of time and difficulty finding common objectives. A CoP can be a useful strategy to facilitate knowledge sharing on disability issues. Future research is necessary to determine strategies of increasing knowledge creation between members.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".