Bibliographic record
Abstract
In this overview chapter, we call upon data from Statistics Canada and the academic literature to present some stylized facts and figures regarding rural older adults and a synthesis of the challenges and opportunities of aging in rural environments. This chapter serves to provide (1) a snapshot of Canadian rural demographic trends, (2) an overview of the state-of-the-art thinking on rural aging, and (3) contextual framing for the in-depth research chapters and vignettes that make up the rural part of this book. Anyone remotely familiar with Canada's geography would not be surprised to learn that by land area, Canada is predominantly rural. Concentrated areas of population cover very little of Canada's expansive 9.9 million square kilometres. Upwards of 90% of the Canadian population live within 160 kilometres of the almost 9,000-kilometre-long Canada–US border (CBC News, 2009). In short, the vast majority of Canada is sparsely populated. Broadly speaking, we consider these sparsely populated places to be rural. Although there is no single perfect definition of a rural environment, rural can be operationally defined as an area with a population density under than 400 people per square kilometre (Channer et al, 2020). Using data from Statistics Canada (2019) population estimates, we found that 8.5 million of Canada's roughly 35 million people live in rural areas. Of those 8.5 million, approximately 1.5 million are aged 65 and over. Like everywhere in Canada, the cohort of Canadians aged 85 and over is growing quickly. Almost 150,000 rural Canadians are 85 years of age or over (Statistics Canada, 2019). Canada's rural population is aging faster than its urban and suburban counterparts. Older Canadians, aged 65 and over, make up 18% of Canada's rural population in comparison to 17% of the suburban population and 15% in urban areas. More than a quarter of Canadians aged 65 or older live in rural areas, as proportionately, the population of older Canadians tends to be higher in rural areas (Menec et al, 2015). Canadian rural populations also have a higher ratio of older adults to working-age adults (known as the old-age dependency ratio). The growing intergenerational imbalance is explained by the dual process of (1) rural youth migration to urban centres for employment, and (2) older Canadians relocating from urban and suburban to rural areas for retirement (Forbes and Hawranik, 2012).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".