Assistive technologies for ageing populations in six low-income and middle-income countries: a systematic review
Bibliographic record
Abstract
Despite the benefits derived from the use of assistive technologies (AT), some parts of the world have minimal or no access to AT. In many low-income and middle-income countries (LMIC), only 5-15% of people who require AT have access to them. Rapid demographic changes will exacerbate this situation as populations over 60 years of age, as well as functional limitations among older populations, in LMIC are expected to be higher than in high-income countries in the coming years. Given both these trends, AT are likely to be in high demand and provide many benefits to respond to challenges related to healthy and productive ageing. Multiple databases were searched for English literature. Three groups of keywords were combined: those relating to AT, ageing population and LMIC selected for this study, namely Brazil, Cambodia, Egypt, India, Turkey and Zimbabwe. These countries are expected to see the most rapid growth in the 65 and above population in the coming years. Results indicate that all countries had AT designed for older adults with existing impairment and disability, but had limited AT that are designed to prevent impairment and disability among older adults who do not currently have any disabilities. All countries have ratified the UN Convention on the Rights of Persons with Disabilities. The findings conclude that AT for ageing populations have received some attention in LMIC as attested by the limited literature results. Analysis of review findings indicate the need for a comprehensive, integrated health and social system approach to increase the current availability of AT for ageing populations in LMIC. These would entail, yet not be limited to, work on: (1) promoting initiatives for low-cost AT; (2) awareness raising and capacity building on AT; (3) bridging the gap between AT policy and practice; and (4) fostering targeted research on AT.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".