The Aging in Place Challenge Program at the National Research Council Canada
Bibliographic record
Abstract
A number of challenge programs to fund research have been initiated at the National Research Council of Canada in the past several years and the outcomes of the research is likely to influence the design of future dwellings and the retrofit of the existing housing stock in Canada. One of these challenge programs addresses the need to support an aging population. It is estimated that by 2051, adults over the age of 65 will represent 25% of the population of Canada. Most aging adults would prefer to age in place in the dwelling or community of their choice. The Aging in Place Challenge program was launched in 2021 with the goal of developing technologies and innovations to support an increase in the number of aging adults who remain in homes and communities of their choice by 2031. The program supports research projects in collaboration with older adults and family caregivers as well as partners in academia, industry, and government toward enabling advancements in AgeTech in Canada. In addition, a number of Aging in Place projects in the Construction Research Center are focused on developing guidance for the design of dwellings intended for successful aging in place. Acoustics, lighting, climate control and other environmental factors all play an important role support the wellbeing of aging adults. The results of the projects from the Construction Research Center will be publicly available guidance for new buildings and the renovation of existing buildings to support aging in place.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.020 |
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".