The Role of Information and Communication Technology (ICT) for Older Adults’ Decision-Making Related to Health, and Health and Social Care Services in Daily Life—A Scoping Review
Bibliographic record
Abstract
Information and communication technology (ICT) can potentially support older adults in making decisions and increase their involvement in decision-making processes. Although the range of technical products has expanded in various areas of society, knowledge is lacking on the influence that ICT has on older adults' decision-making in everyday situations. Based on the literature, we aimed to provide an overview of the role of ICT in home-dwelling older adults' decision-making in relation to health, and health and social care services. A scoping review of articles published between 2010 and 2020 was undertaken by searching five electronic databases. Finally, 12 articles using qualitative, quantitative, and mixed-method designs were included. The articles were published in journals representing biology and medicine, nursing, informatics, and computer science. A majority of the articles were published in the last five years, and most articles came from European countries. The results are presented in three categories: (i) form and function of ICT for decision-making, (ii) perceived value and effect of ICT for decision-making, and (iii) factors influencing ICT use for decision-making. According to our findings, ICT for decision-making in relation to health, and health and social care services was more implicitly described than explicitly described, and we conclude that more research on this topic is needed. Future research should engage older adults and health professionals in developing technology based on their needs. Further, factors that influence older adults' use of ICT should be evaluated to ensure that it is successfully integrated into their daily lives.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".