Essential Features for Empowering NORCs in Your Community: Rapid Review
Bibliographic record
Abstract
Background: Naturally Occurring Retirement Communities (NORCs) have the potential to promote aging in place and establish age-friendly environments. NORCs are geographical areas that have naturally become home to a large concentration of older adults (>30%) but are not purpose-built for older people. NORCs can be enhanced through onsite supports and services, driven by the needs of older residents, but to spread enhanced NORC models, information is needed on how to effectively implement these models across a range of contexts and settings. Current Study and Methods: To further spread and scale enhanced NORC models, we are studying their implementation in 10 sites across Toronto and Barrie. Learnings will be used to create a NORC Implementation Toolkit. In this poster, we outline foundational work to rapidly review and synthesize key features of implementation toolkits. Results: The rapid review illuminated six key elements to be helpful in establishing an enhanced NORC model, 1) Establishing roles and responsibilities of key knowledge users, 2) Conducting a community needs assessment 3) Resident engagement, 4) Optimizing communication strategies, 5) Sound evaluation procedures, and 6) Identifying and minimizing potential barriers. Conclusion: We showcase key features of a NORC implementation toolkit that positions residents as vital stakeholders and active participants in decision-making processes to shape their NORC community. The broader project will help identify how to successfully implement NORC-based interventions in local communities.
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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 | 0.090 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".