REDEFINING THE GOALS OF AGE-FRIENDLY INTERVENTIONS
Bibliographic record
Abstract
Defining the goals of age-friendly interventions is a challenge to both policy and research. Interventions designed to make communities more age-friendly have targeted aspects of transportation, housing, food access, and strengthening social ties among others. Menec et. al. (2011) have suggested that the basic benefit of age-friendly communities is social connectivity, which is associated with these health outcomes. In 2015 a group of researchers came together to discuss and comment on Menec’s framework. This group considered Menec’s framework as it applies to age-friendly efforts, research, and policy. This panel is a follow up to the original discussion. Each presentation represents next steps in considering the implications of the framework for the future of age-friendly efforts. DeLaTorre and Neal, who have been involved with age-friendly efforts for 10 years, compare Menec’s framework to the work being done in Portland Oregon. They consider both similarities and differences in two approaches. Frochen and Pynoos Address housing policy and how housing fits into the age friendly framework. Ring, Glicksman, Kleban and Norstrand present a new methodological approach designed to support Menec’s focus on an ecological framework by using two types of environmental measures – spatial and self-report, to better understand health outcomes. Finally, Lehning and Greenfield will respond by placing the three presentations into a larger policy perspective. Verena Menec et. al.. (2011). Conceptualizing Age-Friendly Communities. Canadian Journal of Aging, 479–493
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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 | 0.147 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".