TRAJECTORY OF AGING COMMUNITIES: THE PATTERNS AND CHARACTERISTICS
Bibliographic record
Abstract
Objectives: While much research has been done on community-dwelling older adults, relatively less attention has been paid to the communities where older adults live. Of all the major cities in Canada, Calgary has the youngest population but the senior population is growing at a pace never seen before. This study examines the trajectory of growth in older adult population at the community level and investigates the characteristics of communities with high density of older adults. Methods: Using the census data from the 1991 to 2016 from Statistics Canada and spatial data from The City of Calgary, all analyses in this study was performed at the census tract level. Using a geographic information system (GIS), we map out how the communities have been changed over the last 25 years and identify the communities where older adults are likely to live. Results: Among the communities in Calgary, there are about 75% of communities with more than 7% of aged 65+ population. The growth in communities with older adults has spread out in the entire city and those communities are characterized as smaller household size, lower levels of incomes, fewer immigration population, lower levels of education, and fewer in the labor force. Discussions: The pattern of growth in communities with older adults and its characteristics have implications that older adults living in these communities might be more vulnerable. More emphasis should be put on community level to provide the purposes of longer lives for older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".