Capturing how age-friendly communities foster positive health, social participation and health equity: a study protocol of key components and processes that promote population health in aging Canadians
Bibliographic record
Abstract
BACKGROUND: To address the challenges of the global aging population, the World Health Organization promoted age-friendly communities as a way to foster the development of active aging community initiatives. Accordingly, key components (i.e., policies, services and structures related to the communities' physical and social environments) should be designed to be age-friendly and help all aging adults to live safely, enjoy good health and stay involved in their communities. Although age-friendly communities are believed to be a promising way to help aging Canadians lead healthy and active lives, little is known about which key components best foster positive health, social participation and health equity, and their underlying mechanisms. This study aims to better understand which and how key components of age-friendly communities best foster positive health, social participation and health equity in aging Canadians. Specifically, the research objectives are to: 1) Describe and compare age-friendly key components of communities across Canada 2) Identify key components best associated with positive health, social participation and health equity of aging adults 3) Explore how these key components foster positive health, social participation and health equity METHODS: A mixed-method sequential explanatory design will be used. The quantitative part will involve a survey of Canadian communities and secondary analysis of cross-sectional data from the Canadian Longitudinal Study on Aging (CLSA). The survey will include an age-friendly questionnaire targeting key components in seven domains: physical environment, housing options, social environment, opportunities for participation, community supports and healthcare services, transportation options, communication and information. The CLSA is a large, national prospective study representative of the Canadian aging population designed to examine health transitions and trajectories of adults as they age. In the qualitative part, a multiple case study will be conducted in five Canadian communities performing best on positive health, social participation and health equity. DISCUSSION: Building on new and existing collaborations and generating evidence from real-world interventions, the results of this project will help communities to promote age-friendly policies, services and structures which foster positive health, social participation and health equity at a population level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".