Developing indicators for evaluation of age-friendlycommunities in Canada: process and results
Bibliographic record
Abstract
INTRODUCTION: In 2006, the World Health Organization launched the Global Age-Friendly Cities Project to support active aging. Canada has a large number of age-friendly initiatives; however, little is known about the effectiveness and outcomes of age-friendly community (AFC) initiatives. In addition, stakeholders report that they lack the capacity and tools to develop and conduct evaluations of their AFC initiatives. In order to address these gaps, the Public Health Agency of Canada developed indicators to support the evaluation of AFC initiatives relevant to a wide range of Canadian communities. These indicators meet the varied needs of communities, but are not designed to evaluate collective impact or enable crosscommunity comparisons. METHODS: An evidence-based, iterative consultation approach was used to develop indicators for AFCs. This involved a literature review and an environmental scan. Two rounds of key expert and stakeholder consultations were conducted to rate potential indicators according to their importance, actionability and feasibility. A final list of indicators and potential measures were developed based on results from these consultations, as well as key policy considerations. RESULTS: Thirty-nine indicators emerged across eight AFC domains plus four indicators related to long-term health and social outcomes. All meet the intended purpose of evaluating AFC initiatives at the community level. A user-friendly guide is available to support and share this work. CONCLUSION: The AFC indicators can help communities evaluate age-friendly initiatives, which is the final step in completing a cycle of the Pan-Canadian AFC milestones. Communities are encouraged to use the evaluation results to improve their AFC initiatives, thereby benefiting a broad range of Canadians.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".