Exploring the Market Requirements for Smart and Traditional Ageing Housing Units: A Mixed Methods Approach
Bibliographic record
Abstract
The world’s population is getting older these days. Frailty, a gerontologic health condition associated with ageing, has serious consequences. One crucial remedy for the elderly population is the development of ageing-in-place infrastructures. To better understand the market requirements for ageing housing units, the causes of downsizing and the governmental measures to ameliorate the situation, face-to-face in-depth individual and focus group interviews were conducted in this study. Elderly residents of two significant ageing-in-place institutions in Hong Kong, along with their caregivers, were interviewed. The method of methodological triangulation was used to combine interviews, records, and communication tools to increase the reliability and trustworthiness of the findings. The provision of facilities for the elderly has successfully established a pathway for creating and making housing spaces available to families who need larger homes, while the elderly typically downsize from larger homes and relieve their financial needs. It is also found that a digital divide exists; some respondents suggested that they do not know about computers and do not use smart facilities in their homes.
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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 | 0.034 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".