Building Capacity and Community to Improve the Quality of Life of Seniors: In Defiance of Aging
Bibliographic record
Abstract
Research Framework: The aging of the population, unprecedented in human history, is a complex reality of the 21st century. This complexity is rooted not only in social and biological, but also in quantitative phenomena. Population aging is in the strict sense a structural effect, an increase in the proportion of the elderly in a given population, while the increase in aged persons is an effect of flux, which quantifies the increase in the number of seniors, those 65 years of age or older (Dumont, 2018a). Although its four main causes (increased life expectancy, decreased number of births, migration, demographic changes) are unanimously accepted by researchers; the manifestations, consequences and responses to aging are far from homogeneous (Breton et Temporal, 2019; Blanchet, 2013; Dumont, 2006; Simard, 2010). Indeed, the consequences of aging differ from one region to another, forcing us to understand population issues in targeted and specific ways, socially, economically and geopolitically (Dumont, 2016a, 2018b; Saillant, 2016; Gucher, 2012; Hodge, 2008). All of society’s institutions are affected by the challenges of an aging population: policies, employment, work, health, family, social security, regional management and development, and even democratic functioning. These diverse issues influence both seniors’ quality of life (Rican et al., 2013) and collaborative and regional governance. \n \nObjectives: To identify the main issues and challenges associated with population aging in improving the social inclusion and quality of life of seniors. These issues and challenges relate to seniors' income, accessibility of and proximity to local services, equipment and infrastructure, elder care and workforce planning. \nMethodology: This article draws on the different contributions in this thematic issue and on the expertise of the three authors. In addition, based on a literature review, we advocate a content analysis, which will be combined with empirical data found mainly, but not exclusively, in various Statistics Canada documents. \n \nResults: Most of these issues and challenges originate at the grassroots level, local or regional. However, their implementation requires energetic top-down action, as local and regional leaders, despite their good intentions, do not have all of the required tools and means to address them. \n \nConclusions: A regional policy on aging must be implemented that considers the local and regional characteristics of the environment concerned and the needs expressed by seniors and their families. Thus, environments must be created that are conducive to improving the quality of life and vitality of both the elderly and those around them. In addition, cross-functional gerontological actions must be initiated that involve partnering with both endogenous and exogenous stakeholders, and that consider the regional diversity of aging. The objective is to ensure that people remain active in society as they age – a prerequisite for preserving their health. \nContribution: From an academic viewpoint, our presentation, like that of other authors in this issue, is based on three closely interrelated endogenous models. These are capacity building and empowerment of stakeholders, collaborative governance, and progressive local development. Although these different models are effective channels for stimulating local initiatives, and in particular social innovations that bring about social change, they do not address the multiple challenges associated with active and healthy aging, which require cross-functional interventions rolled out at the regional level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".