Housing and care in later life: Breaking down silos
Bibliographic record
Abstract
In Quebec, the over eighty population has almost quintupled between 1970 and 2010. Although nobody ages in the same way or at the same pace, elderly people inevitably experience a gradual weakening. A difficult choice that older adults with severe loss of autonomy will eventually have to make is whether to stay in their home or relocate. However, several aspects of the built environment they live in are associated with their quality of life and well-being. Action must thus be taken to ensure that seniors can make informed choices about housing options that are not only comfortable and safe, but deemed desirable. This requires building bridges between diversified research and intervention areas, namely those of health and welfare and those of architecture and planning. Such action would contribute to the integration of existing scientific evidence and to identify gaps in knowledge that need to be filled, as well as to bring together scientific and professional cultures that do not usually work together, and to give a voice to key knowledge users, that is, elderly people and their caregivers. This article reports the results of a collaboration begun in the summer of 2015 between researchers from three Canadian universities in Quebec and Alberta, and diverse knowledge users, around the issue of housing and care in old age, in order to tackle the many challenges associated with bringing closer these research and intervention 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".