The ageing population and implication to product design
Bibliographic record
Abstract
Data from the World Health Organization (WHO) showed that the total number of people aged 65 and older is expected to increase from 300 million to 540 million in 1990 to 2015, and by the year 2025 to 1.2 billion. Progressive ageing of the human population constitutes a serious challenge for the contemporary civilization. Old age is becoming one of the most urgent social problems that must be solved for human and economic reasons. It is estimated that by the year 2020, a fifth to a quarter of the developed world population will be over 65 years old. The trend towards an aging society is also seen in various developing nations. Thus, the aged will comprise a large proportion of the population. As the life expectancy of the population increases, it is imperative that self-sufficiency of the elderly is prolonged. The design of products fit for their use plays an increasingly important role in how they manage their daily activities. Efforts must be made to design products for the elderly that enhances their comfort, safety and increase the likelihood of them remaining independent in their home and community. There is the need to design products taking the functional capabilities and limitations of aged into consideration. Studies have shown that ageing is related to changes in stature and weight. The size and shape of the body also changes as a person gets older. These physiological changes taking place as one aged require that anthropometric dimensions of the elderly be measured. The design of functional space and articles for daily use must comply with physical and functional limitations of the elderly. Appropriate anthropometric data can be used to design for reach, clearance, strength and posture. The mean and standard deviation of the Malaysian elderly female anthropometric data are presented in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".