Mobility Patterns of the Disabled in Montreal in 1998 and Long Term Perspectives
Bibliographic record
Abstract
Mobility patterns of the disabled is generally not well known because of fault of data in O-D surveys (when they exist), which do not have sufficient samples to permit detailed analysis of the disabled. The EQLA survey of Quebec of 1998 (Quebec Survey on Limitations of Activities) is part of a more general Health Survey and was done from a sub sample of persons having declared a handicap in the general census of 1996. EQLA contains precise questions on transportation patterns of the disabled and has a good sample (4,015 respondents having a handicap). The paper describes mobility patterns in the Montreal administrative region (or Island of Montreal) and in the Province of Quebec of persons having a handicap compared to the general population to diagnose access to mobility of the handicapped and analyzes the main reasons which impair mobility, namely lack of supply. Considering the strong ageing in the next decades, these results are then discussed in a long term perspective. Most countries or regions which had a strong baby boom will encounter rapid ageing in the next two or three decades. This is true in Quebec where the baby boom was strong, followed by a spectacular decrease of natality in the sixties. In Montreal, the authors expect that the proportion people 65 and more will reach around 26.5% in 2030 (from 13% in 2001), not only in the central city but also in the low density suburbs, more difficult to service. Since the frequencies and severity of handicaps augment quasi exponentially with ageing, the possible impacts of such trends on future demand and supply is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".