Perceptions of and needs for e‐Health solutions for elderly people with cognitive impairment, their caregivers and health care providers: A qualitative exploration
Bibliographic record
Abstract
Abstract Background The prevalence of mild cognitive impairment (MCI) and mild neurocognitive disorder (mNCD) are steadily increasing in Canada. Information and communication technologies (ICTs) in health represent an innovation to promote home care and autonomy for people with various degrees of cognitive impairment. The objective of this study is to develop a web‐based multicriteria decision support tool adapted to older adults with MCI or mNCD, their informal caregivers, and health care providers (HCPs) to support the development and implementation of ICTs adapted to the needs and preferences of people with cognitive impairments and their caregivers. Methods We used a participatory research strategy to develop of a decision support tool for the use of ICTs focused on the needs of patients, their caregivers, and HCPs. Data collection consisted of semi‐structured interviews with elderly people with MCI (N = 10) and caregivers of people with mNCD (N= 7) to explore their current knowledge and perceptions of various ICTs as well as their needs and preferences for such interventions and a focus group with HCPs to understand their perceptions of the needs of seniors with MCI and caregivers of people with mNCD. Results ICTs are seen as a beneficial solution to promote home care and autonomy for people with cognitive disorders. ICTs provide a sense of security and peace of mind, especially for caregivers of people with mNCD. However, the complexity and high cost of ICTs as well as the lack of support appear to be major limits to their use. HCP recognize the value of e‐Health but claim to lack reliable information and were therefore highly unsure to recommend its use. Conclusions People suffering from cognitive disorders and their caregivers are generally open to technological developments and favour the use of ICTs. For health professionals, continuous training on ICTs would make them more comfortable to recommend them to patients and their families. Although the use of ICTs is promising for maintaining elderly people with cognitive disorders at home, our study shows that it will be necessary to find ways to make them accessible to promote their use.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".