Convivialité des municipalités canadiennes à l’égard des aînés : portrait et facteurs associés
Bibliographic record
Abstract
OBJECTIVES: This study aimed to (1) document, globally and by domain, Canadian municipalities' level of age-friendliness, and (2) identify municipality characteristics most associated with age-friendliness. METHODS: A cross-sectional survey was sent to all Canadian municipalities (N=3406) with an online survey of 56 items from 9 domains providing age-friendliness scores. These scores were then crossed with the following municipality characteristics: percentage of adults aged 65 and older, population density, material deprivation, social deprivation, degree of metropolitan influence, implementation step of an age-friendly municipality initiative and geographic area. RESULTS: Nine hundred twenty-one municipalities completed the survey. Overall, municipalities' age-friendliness total score is good (58.4%). Four domains have high scores: Security (80.0%), Respect and social inclusion (65.0%), Outdoor spaces and building (62.2%), and Social participation (62.2%). Higher age-friendliness is associated with metropolitan municipalities, regions other than Prairies and Atlantic, higher residential density, greater proportion of older adults, greater social deprivation, lower material deprivation, and the last step of an age-friendly initiative. CONCLUSION: This portrait of Canadian municipalities' age-friendliness can be used to strengthen actions promoting active aging.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".