Bibliographic record
Abstract
Population ageing is a worldwide phenomenon, especially in Western world. By 2030, nearly a quarter of people in the United Kingdom and the Netherlands will be aged 60 or older. As people age, their mobility often declines due to physical and cognitive challenges, which may negatively impact their health. Mobility involves how older adults move and travel, including periods of rest and stillness. Maintaining mobility is important for healthy ageing, but current research and policies often emphasise movement without acknowledging natural mobility decline. This approach can lead to the perception that less mobile older adults are ageing unhealthily. This thesis aims to explain mobility behaviours in relation to health among older adults living at home in the Netherlands and the United Kingdom. Four studies were conducted with older adults, using innovative methods, such as GPS tracker, pedometer and surveys to examine indoor and outdoor movements, micro-movements, proximity to amenities, and the ability to move and perform daily tasks. The results highlight the importance of understanding small everyday movements and their role in health. This research advocates for a balanced approach to mobility in later life, promoting both movement and rest for healthier ageing.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".