Co-design knowledge mobilization tools for universal accessibility in municipalities
Bibliographic record
Abstract
Introduction: Modern research teams are re-evaluating conventional methods with the aim of improving the usefulness of knowledge for users, focusing on the role of knowledge users in shaping innovation. In disability field, encouraging participatory research inherently involves diverse perspectives and inclusion, which aligns with the principles of universal accessibility. By actively involving individuals with various backgrounds, abilities, and needs in the research process, we can better understand and address the challenges faced in adopting universal accessibility. This approach ensures that solutions are more comprehensive, inclusive, and effectively cater to the needs of all individuals, fostering a more equitable and accessible environment for everyone. Despite municipal organizations mandating universal accessibility action plans, they lack tools for efficient implementation. The aim of this study was to develop knowledge mobilization tools tailored to a specific municipal context in Quebec, Canada, to facilitate the implementation of universal accessibility measures by municipal employees. Methods: The co-design process employed in this study was organized into four distinct stages, following the Morales model: (1) Exploration (2) Co-Design (3) Validation (4) Development. Results: Stages one and two highlighted the employees' lack of awareness about universal accessibility issues and their need to have more information and resources about how universal accessibility is encountered in their work. A steering committee co-designed three video vignettes about universal accessibility, the city's action plan and measures included in it. Discussion: The co-design approach used in this study allowed us to observe the non-linear nature of partnership research with an organization as complex as a municipality. Our study shows significant advantages of collaboration between the municipal sector and research.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".