Perceived Training Needs of Municipal Stakeholders in Quebec (Canada) Relating to Universal Design Action Plans
Bibliographic record
Abstract
Quebec (Canada) municipalities with ≥15 000 inhabitants are legally required to produce an annual action plan to reduce barriers encountered by person with disabilities. Actual tools for universal design are diverse and not harmonised between cities, leading to important training needs. We thus aimed to identify priority training needs among municipalities of all sizes. We use a two-phase sequential descriptive design starting with an online survey (Phase 1) anchored into dimensions of inclusive access followed by focus group discussions (Phase 2). Descriptive statistics and a semi-inductive content analysis for qualitative data were used. A total of n = 114 municipalities responded to Phase 1 including nearly half (37/78) of municipalities with a population ≥15 000 inhabitants. The top five priority needs were 1) Needs assessment, 2) General knowledge, 3) Practical and organisational knowledge, 4) Design/planning phase and 5) Know-how, attitudes, mentalities, culture of the municipalities. Participants (n = 10) to Phase 2 insisted on their needs for practical knowledge, including authentic, contextualised examples coming from other cities. No major differences in needs to prioritise emerged when contrasting larger and smaller size’s municipalities. Results highlighted a variety of training needs, including the importance of prioritising practical contextualised knowledge anchored in authentic experience.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".