<i>“WE CAN ALL CONTRIBUTE IN OUR OWN WAY”</i> : KNOWLEDGE MOBILIZATION TOOLS TO PROMOTE BEST PRACTICES IN UNIVERSAL ACCESSIBILITY
Bibliographic record
Abstract
Abstract Background Cities aim to enhance urban accessibility following the adoption of the United Nations’ Convention on the Rights of Persons with Disabilities. However, implementation faces challenges due to complex municipal legislation, lack of awareness, and organizational obstacles. Engaging stakeholders and empowering municipal employees through knowledge mobilization is crucial, as shown in a Quebec City’s partnership research process. Aim To report the implementation strategy as implemented, explore the perception of the employees about the format and feasibility of the implementation strategy and explore the induced changes of knowledge mobilization tools on the implementation determinants of universal accessibility measures for municipal employees. Methods The study used a multi-method design, involving interviews and a questionnaire with the project steering committee, made up of city employees and the research team. Three 30-minute participatory workshops were conducted for culture, communications, and public consultation administrative units. Results Participants appreciated the workshop format and video content, suggesting minor improvements for broader implementation. The tools effectively increased engagement in implementing universal accessibility measures, proving valuable for raising awareness. Discussion and Conclusion The study demonstrates the advantages of a collaborative approach in developing knowledge mobilization tools, enhancing municipal personnel’s capacity for universal accessibility measures, and highlighting the need for adaptable strategies. Contributions to the litterature Knowledge mobilization tools created in partnership with knowledge users encourage buy-in and a positive view of the tools. An interactive implementation strategy actively involving knowledge users promotes awareness and behavior change Municipal organizations’ context being complex, the implementation strategy must be adapted to each group of people and their reality to facilitate the implementation and adoption of the tools. The combined use of a theoretical framework and a participatory approach provides a guideline for the development of tools and implementation, while adapting to the specific context.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".