PUBLIC SECTOR INVOLVEMENT IN AGE-FRIENDLY CITIES AND COMMUNITIES: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract The global age-friendly cities and communities (AFCC) movement has long centered the involvement of the public sector, calling on high-ranking authorities to commit to improving the built, social, and service environments of their localities. However, there is little understanding of how the public sector is involved in actual practice. To address this gap, we conducted a scoping review of peer-reviewed articles published since 2010, with a focus on studies in Canada and the United States, to synthesize the evidence on the ways in which the public sector has been involved with AFCC work. Our review identified four primary themes. The first theme describes the variety across studies in descriptions of the overall positioning of the public sector in AFCC efforts, with public sector actors ranging from leaders to partners to targets for advocacy. The second theme addresses activities that are initiated by a public administration that targets age-friendly improvements for that administration. The third theme encompasses activities that help to connect a public administration with, as well as influence, outside actors, primarily the private sector and government administrations at other systems levels. The final theme elucidates the various ways in which the public sector is involved with AFCC assessment, evaluation, and research. We discuss the implications of our findings for practice, policy, and continued empirical research. We also discuss theoretical implications for AFCC efforts in terms of distinguishing “whole of government” from “whole of society,” grassroots, and planning-specific approaches.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".